-> If you use Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset please cite: https://dergipark.org.tr/en/pub/cukurovaummfd/issue/28708/310341

@research article { cukurovaummfd310341, journal = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi}, issn = {1019-1011}, eissn = {2564-7520}, address = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi Yayın Kurulu Başkanlığı 01330 ADANA}, publisher = {Cukurova University}, year = {2016}, volume = {31}, pages = {464 - 482}, doi = {10.21605/cukurovaummfd.310341}, title = {Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme}, key = {cite}, author = {Gözükara, Furkan and Özel, Selma Ayşe} }

https://doi.org/10.21605/cukurovaummfd.310341

-> Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset is composed as below: ->-> Top 50 E-commerce sites in Turkey are crawled and their comments are extracted. Then randomly 2000 comments selected and manually labelled by a field expert. ->-> After manual labeling the selected comments is done, 600 negative and 600 positive comments are left. ->-> This dataset contains these comments.

-> English_Movie_Reviews_by_Pang_and_Lee_2004 ->-> Pang, B., Lee, L., 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts, In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | polarity dataset v2.0 - review_polarity.tar.gz

-> English_Movie_Reviews_Sentences_by_Pang_and_Lee_2005 ->-> Pang, B., Lee, L., 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 115-124), Association for Computational Linguistics ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | sentence polarity dataset v1.0 - rt-polaritydata.tar.gz

-> English_Product_Reviews_by_Blitzer_et_al_2007 ->-> Article of the dataset: Blitzer, J., Dredze, M., Pereira, F., 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, In ACL (Vol. 7, pp. 440-447). ->-> Source: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/ | processed_acl.tar.gz

-> Turkish_Movie_Reviews_by_Demirtas_and_Pechenizkiy_2013 ->-> Demirtas, E., Pechenizkiy, M., 2013. Cross-lingual polarity detection with machine translation, In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 9). ACM. ->-> http://www.win.tue.nl/~mpechen/projects/smm/#Datasets Turkish_Movie_Sentiment.zip

-> The dataset files are provided as used in the article. -> Weka files are generated with Raw Frequency of terms rather than used Weighting Schemes

-> The folder Cross_Validation contains 10-fold cross-validation each fold files. -> Inside Cross_Validation folder, each turn of the cross-validation is named as test_X where X is the turn number -> Inside test_X folder * Test_Set_Negative_RAW: Contains raw negative class Test data of that cross-validation turn * Test_Set_Negative_Processed: Contains pre-processed negative class Test data of that cross-validation turn * Test_Set_Positive_RAW: Contains raw positive class Test data of that cross-validation turn * Test_Set_Positive_Processed: Contains pre-processed positive class Test data of that cross-validation turn * Train_Set_Negative_RAW: Contains raw negative class Train data of that cross-validation turn * Train_Set_Negative_Processed: Contains pre-processed negative class Train data of that cross-validation turn * Train_Set_Positive_RAW: Contains raw positive class Train data of that cross-validation turn * Train_Set_Positive_Processed: Contains pre-processed positive class Train data of that cross-validation turn * Train_Set_For_Weka: Contains processed Train set formatted for Weka * Test_Set_For_Weka: Contains processed Test set formatted for Weka

-> The folder Entire_Dataset contains files for Entire Dataset * Negative_Processed: Contains all negative comments processed data * Positive_Processed: Contains all positive comments processed data * Negative_RAW: Contains all negative comments RAW data * Positive_RAW: Contains all positive comments RAW data * Entire_Dataset_WEKA: Contains all documents processed data in WEKA format

Dataset Card for amazon reviews for sentiment analysis

  Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/amazon-reviews-sentiment-analysis.

Models for determining the sentiment analysis of text reviews for subsequent improvement of the company's service. Moreover, this service can be extrapolated from the banking sector to insurance and any sphere where there is a reflection of users, as well as the existence of a platform for posting reviews and the availability of reviews themselves (according to the principle of the more reviews, the better). The service will allow you to find the causes of negativity and give them both a social and economic assessment.

The reported study was funded by RFBR, project number 20-310-70042.

Models are applicable only to the Russian-speaking community, since the models were trained on Russian semantics.

Model weights (CNN) stored in files: cnn-frozen-embeddings-08-0.94.hdf5 and n-trainable-03-0.94.hdf5 (CNN with frozen embedding layer, and defrosted retrained, respectively) responses_model.w2v, w2v_xgboost.w2v - saved Word2Vec models of reviews. xgb_model.pickle and nn_model.pickle are trained models.

Dataset

This dataset was created by Buddhadeb Mondal

Contents

Online Sentiment Analysis Tool Market Outlook

As of 2023, the global online sentiment analysis tool market size was valued at approximately $2.5 billion and is projected to reach around $8.4 billion by 2032, exhibiting a CAGR of 14.2% during the forecast period. The growth of this market is primarily driven by the increasing need for businesses to understand customer sentiment, enhance customer experiences, and make data-driven decisions. Factors such as the surge in social media usage, advancements in artificial intelligence, and the growing adoption of cloud-based solutions have significantly contributed to the expansion and evolution of the online sentiment analysis tool market.

One of the key growth factors in the online sentiment analysis tool market is the exponential rise in social media platforms and online reviews. Businesses now have access to a wealth of customer opinions and feedback, which necessitates the use of advanced tools to analyze vast amounts of unstructured data. Social media platforms such as Twitter, Facebook, and Instagram have become critical touchpoints for understanding customer sentiment, making sentiment analysis tools indispensable for modern businesses. Furthermore, the ability to quickly gauge public opinion on products, services, and brands helps companies in developing timely and effective marketing strategies.

Another significant growth factor is the technological advancements in natural language processing (NLP) and machine learning. These technologies have enhanced the accuracy and efficiency of sentiment analysis tools, allowing for more precise interpretation of human emotions and opinions expressed online. The integration of AI and machine learning algorithms enables these tools to continually improve by learning from new data, thereby offering more reliable and actionable insights. This technological progress is expected to drive the adoption of online sentiment analysis tools across various sectors, further boosting market growth.

Moreover, the increasing emphasis on customer experience management (CEM) is acting as a catalyst for the market's growth. In today's competitive business environment, understanding and improving customer experience is paramount. Sentiment analysis tools provide businesses with critical insights into customer preferences, pain points, and overall satisfaction levels. By leveraging these insights, companies can tailor their products, services, and customer interactions to enhance overall customer satisfaction and loyalty. This growing focus on CEM is expected to fuel the demand for advanced sentiment analysis tools in the coming years.

From a regional perspective, North America currently holds the largest share in the online sentiment analysis tool market, driven by the presence of major technology companies and a high level of digital adoption. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid proliferation of smartphones, increasing internet penetration, and the expanding e-commerce industry. Europe also presents significant growth opportunities, with many businesses in the region increasingly adopting sentiment analysis tools to improve customer engagement and competitive advantage.

Component Analysis

Analyzing the market by component, we see it divided primarily into software and services. The software segment is expected to hold the lion's share of the market during the forecast period. This is largely due to the increasing adoption of advanced analytics software that incorporates artificial intelligence and machine learning for improved sentiment detection and analysis. Companies are investing heavily in developing sophisticated software solutions capable of processing large volumes of data with high accuracy. The continuous innovation in this segment ensures that businesses have access to cutting-edge tools that can provide deep insights into customer sentiment.

In contrast, the services segment, which includes consulting, training, maintenance, and support services, is also experiencing substantial growth. As companies invest in sentiment analysis tools, they often require expert guidance to effectively implement and utilize these technologies. Services such as customized training programs and ongoing maintenance ensure that organizations can maximize the potential of their sentiment analysis tools. Furthermore, consulting services help businesses tailor these tools to meet specific needs, such as sector-specific sentiment analysis, which adds a layer of specialization and effectiveness to the overall

Sentiment Analysis Software Market Outlook

The global sentiment analysis software market size was valued at approximately $3.5 billion in 2023 and is projected to reach around $8.7 billion by 2032, growing at a CAGR of 10.8% during the forecast period. The burgeoning growth of this market is largely attributed to the increasing need for actionable insights into consumer behavior and preferences, which is driving enterprises to adopt sentiment analysis tools. The relentless expansion of digital business operations and the integration of advanced analytics to understand customer sentiment further augment market growth. The demand for real-time sentiment analysis is becoming a crucial component for businesses aiming to enhance customer experience and tailor their products and services accordingly.

One of the primary growth factors for the sentiment analysis software market is the rapid adoption of social media platforms and the proliferation of digital content. With consumers increasingly expressing their opinions and preferences online, businesses are compelled to utilize sentiment analysis tools to sift through massive volumes of data and derive meaningful insights. This trend is further fueled by the need for businesses to maintain a competitive edge by understanding market trends and consumer sentiment. Additionally, the integration of machine learning and natural language processing technologies into sentiment analysis software is enhancing its accuracy and efficiency, thereby boosting its adoption across various industries.

Moreover, the market is experiencing significant growth due to the rising demand for customer experience management solutions. With customer satisfaction becoming a pivotal focus for businesses, sentiment analysis software is being leveraged to monitor and analyze customer feedback in real-time. This allows companies to make informed decisions and implement strategies that improve customer engagement and loyalty. The ability to anticipate customer needs and preferences through sentiment analysis is facilitating improved service delivery and product innovation, further driving the market's expansion.

Furthermore, the increasing adoption of cloud-based deployment models is also contributing to the market's growth. Cloud-based sentiment analysis solutions offer scalability, flexibility, and cost-effectiveness, making them ideal for businesses of all sizes. The ease of integration with existing systems and the ability to access insights remotely are encouraging organizations to transition from traditional on-premises solutions to cloud-based platforms. This shift is particularly beneficial for small and medium enterprises (SMEs) that seek to harness the power of sentiment analysis without incurring significant infrastructure costs.

Regionally, North America continues to dominate the sentiment analysis software market, driven by the presence of major technology companies and high adoption rates of advanced analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, propelled by increasing digitalization and the expanding e-commerce sector. Emerging economies in this region are embracing sentiment analysis tools to better understand consumer preferences and enhance competitiveness in the global market. Europe and Latin America are also witnessing significant growth, supported by technological advancements and a growing focus on improving customer satisfaction.

Component Analysis

The sentiment analysis software market is segmented into software and services, each playing a critical role in the adoption and implementation of sentiment analysis solutions. The software segment dominates the market, driven by the increasing demand for standalone and integrated solutions that offer capabilities such as text analytics, predictive analytics, and visualization tools. These software solutions are designed to cater to the diverse needs of businesses across various industries, providing them with the ability to analyze vast amounts of unstructured data efficiently.

Within the software segment, the integration of artificial intelligence (AI) and machine learning algorithms is a significant trend that is enhancing the functionality and accuracy of sentiment analysis tools. These technologies allow software solutions to learn from data, improve over time, and provide more precise insights into consumer sentiment. This is particularly beneficial for businesses that deal with large data volumes and require real-time analysis to make informed decisions. As a result, the demand for advanc

The global sentiment analysis market, encompassing devices and software solutions, is experiencing robust growth, driven by the increasing need for businesses to understand customer opinions and preferences. The market's expansion is fueled by the proliferation of social media, e-commerce platforms, and customer service interactions generating massive amounts of unstructured data. Businesses are leveraging sentiment analysis to extract valuable insights from this data, improving customer service, product development, brand reputation management, and marketing strategies. The market's segmentation reveals a strong demand across diverse applications, including customer feedback analysis, brand monitoring, market research, and risk management. Technological advancements, such as the development of more sophisticated natural language processing (NLP) algorithms and machine learning models, are further accelerating market growth. Competition is intense, with established players like IBM, Microsoft, and AWS alongside specialized sentiment analysis vendors. While precise market size figures are unavailable, considering the growth of related technologies like AI and big data analytics, a reasonable estimate for the 2025 market size for sentiment analysis devices (excluding software-only solutions) could be placed at $5 billion, with a Compound Annual Growth Rate (CAGR) of approximately 15% projected through 2033. This growth is tempered by factors such as the complexity of accurately interpreting sentiment, data privacy concerns, and the need for high-quality data for accurate analysis. The European market, particularly the UK, Germany, and France, is expected to represent a significant portion of this market share due to the region's strong digital economy and adoption of advanced technologies. The future of the market hinges on overcoming these restraints and continuing advancements in NLP and AI, expanding the accuracy and accessibility of sentiment analysis devices for a wider range of businesses and applications.

Context

This is a small subset of dataset of Book reviews from Amazon Kindle Store category.

Content

5-core dataset of product reviews from Amazon Kindle Store category from May 1996 - July 2014. Contains total of 982619 entries. Each reviewer has at least 5 reviews and each product has at least 5 reviews in this dataset. Columns - asin - ID of the product, like B000FA64PK -helpful - helpfulness rating of the review - example: 2/3. -overall - rating of the product. -reviewText - text of the review (heading). -reviewTime - time of the review (raw). -reviewerID - ID of the reviewer, like A3SPTOKDG7WBLN -reviewerName - name of the reviewer. -summary - summary of the review (description). -unixReviewTime - unix timestamp.

Which file to use?

There are two files one is preprocessed ready for sentiment analysis and other is unprocessed to you basically have to process the dataset and then perform sentiment analysis

Acknowledgements

This dataset is taken from Amazon product data, Julian McAuley, UCSD website. http://jmcauley.ucsd.edu/data/amazon/

License to the data files belong to them.

Inspiration

-Sentiment analysis on reviews. -Understanding how people rate usefulness of a review/ What factors influence helpfulness of a review. -Fake reviews/ outliers. -Best rated product IDs, or similarity between products based on reviews alone (not the best idea ikr). -Any other interesting analysis

The global Sentiment Analysis Software market is projected to reach a market size of USD 1930.6 million by 2033, expanding at a CAGR of 13.0% from 2025 to 2033. The increasing adoption of sentiment analysis software across various industries, such as retail, BFSI, and healthcare, to understand customer feedback and make informed decisions is driving the market growth. Additionally, the rising need for real-time feedback analysis, the growing volume of unstructured data, and the advancements in artificial intelligence and machine learning technologies are contributing to the market expansion. North America is expected to dominate the global Sentiment Analysis Software market throughout the forecast period. The region's well-established IT infrastructure, the presence of major technology companies, and the high adoption rate of advanced technologies are driving the market growth in North America. The Asia Pacific region is projected to witness significant growth during the forecast period due to the increasing adoption of sentiment analysis software in developing economies such as China and India. The growing retail and e-commerce sectors in these countries are also expected to contribute to the market expansion in the Asia Pacific region.

The customer review marketing market, valued at $1340.9 million in 2025, is poised for significant growth. This expansion is driven by several key factors. The increasing reliance on online reviews for purchase decisions by consumers fuels demand for effective review marketing strategies. Businesses across all sectors, especially e-commerce giants like Amazon and Alibaba, recognize the crucial role of positive online reviews in brand building, customer acquisition, and sales conversion. The market’s segmentation, encompassing online and offline review marketing for both physical and virtual products, presents diverse opportunities for specialized service providers. Furthermore, technological advancements enabling automated review generation and analysis, along with improved sentiment analysis tools, are enhancing market efficiency and fueling adoption. Growth is also observed across diverse geographical regions, with North America and Asia-Pacific expected to be major contributors due to high internet penetration and e-commerce maturity. However, the market faces certain challenges. The proliferation of fake reviews poses a significant threat, eroding consumer trust and necessitating robust verification mechanisms. Moreover, managing and responding to negative reviews effectively requires significant resources and expertise, posing a barrier for smaller businesses. Maintaining data privacy and complying with evolving regulations around review collection and usage is another crucial consideration for companies operating in this space. Despite these hurdles, the overall market trajectory indicates robust growth, propelled by the increasing importance of online reputation management and the continued expansion of e-commerce globally. The competitive landscape, featuring both established players and emerging service providers, suggests a dynamic environment with opportunities for both large corporations and specialized niche players.

E-Commerce Customer Support Conversations Dataset Summary: This dataset contains customer support queries and responses from an e-commerce context. It is designed for training and fine-tuning AI models for automated customer service, chatbots, and natural language processing (NLP) applications. Use Cases: Fine-tuning conversational AI models (e.g., GPT, BERT) Training chatbots for e-commerce support Improving customer service automation Sentiment and intent analysis Dataset Format: The… See the full description on the dataset page: https://huggingface.co/datasets/Venkatrajan247/ecommerce-customer-support-conversations.

Usecase/Applications possible with the data:

Customer feedback analysis: Analyzing customer feedback can be helpful for businesses to keep customers happy, stay loyal to the brand, and identify any areas to improve.

Social media monitoring: With sentiment analysis, companies can monitor what's being said about them on social media and use that to figure out how people feel about their products and services and track any new trends.

Market research: Sentiment analysis can be used to analyze market trends and consumer preferences, which can help companies make informed business decisions and develop effective marketing strategies.

Financial analysis: You can use sentiment analysis to determine what people say about the stock market through news and social media, which can help you make investing decisions.

For e-commerce (amazon/Bestbuy/home depot and much more) following data fields can be included: Title Price Vendor Name Ratings Reviews Brand ASIN URL Sentiment analysis for each review And other fields, as per request

The global text information processing platform market is experiencing robust growth, driven by the increasing need for efficient data analysis across various sectors. The market, valued at approximately $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The rise of big data and the proliferation of unstructured textual data across industries like finance, e-commerce, and healthcare necessitate sophisticated tools for processing and extracting meaningful insights. Furthermore, advancements in natural language processing (NLP) and machine learning (ML) are enhancing the capabilities of these platforms, enabling more accurate sentiment analysis, topic modeling, and automated report generation. The growing adoption of cloud-based solutions and the increasing demand for real-time data analytics further contribute to market growth. While data security concerns and the need for skilled professionals pose some challenges, the overall market outlook remains exceptionally positive. Segmentation reveals significant opportunities within specific application areas. The financial services industry leverages text information processing for fraud detection, risk assessment, and regulatory compliance. E-commerce relies on these platforms for customer sentiment analysis, market research, and personalized recommendations. The healthcare industry utilizes them for analyzing patient records, medical literature, and research papers to improve diagnosis and treatment. Different platform types cater to specific needs; Natural Language Processing platforms offer broad capabilities, while Text Mining and Public Opinion Analysis platforms address specialized tasks. The competitive landscape is dynamic, with established players like IBM, Google, Microsoft, and Amazon competing alongside specialized vendors like OpenText, SAS, and smaller innovative companies. Geographic distribution indicates strong growth in North America and Europe, fueled by early adoption and advanced technological infrastructure. However, Asia Pacific is emerging as a significant growth region, driven by rising digitalization and increasing data volumes. The continued expansion of digital technologies and the growing need for data-driven decision-making will ensure sustained growth in the text information processing platform market in the coming years.

This dataset comprises 6,020 consumer reviews from five major Chinese museum flagship stores, evaluating home-themed cultural products across functional, cultural, and social value dimensions. Collected via Python crawlers and processed with text mining techniques (including sentiment analysis and LDA topic modeling), it supports research on consumer behavior, product design, and NLP applications.

This dataset comprises customer reviews for Amazon, an online retail giant, featuring insights into customer experiences, including ratings, review titles, texts, and metadata. It is valuable for analyzing customer satisfaction, sentiment, and trends.

Column Descriptions:

Reviewer Name: Identifies the reviewer. Profile Link: Links to the reviewer's profile for additional insights. Country: Indicates the reviewer's location. Review Count: Number of reviews by the same user, showing engagement level. Review Date: When the review was posted, useful for time analysis. Rating: Numerical satisfaction measure. Review Title: Summarizes the review sentiment. Review Text: Detailed customer feedback. Date of Experience: When the service/product was experienced.

Prospective applications:

Sentiment Analysis: Analyze review texts and titles to assess overall customer sentiment toward products, enabling the identification of strengths and weaknesses. Customer Satisfaction Tracking: Track and visualize rating trends over time to understand fluctuations in customer satisfaction. Product Improvement: Identify common themes in reviews to highlight areas for product enhancement or development. Market Segmentation: Use country and demographic information to customize marketing strategies and gain insights into regional preferences. Competitor Analysis: Evaluate customer feedback on Amazon products in comparison to competitors to determine market positioning. Recommendation Systems: Leverage review data to enhance recommendation algorithms, improving personalized shopping experiences. Trend Analysis: Investigate temporal patterns in reviews to link sentiment changes with marketing efforts or product launches.

This extensive dataset serves as a valuable asset for various analyses focused on enhancing customer engagement and refining business strategies.

AI Sentiment Analysis Tool Market Outlook

The global AI Sentiment Analysis Tool market size is projected to grow from USD 4.1 billion in 2023 to USD 11.8 billion by 2032, at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This impressive growth can be attributed to a surge in the adoption of AI technologies across various industries looking to harness the power of real-time sentiment analysis for better decision-making and enhanced customer experiences.

One of the primary growth factors driving the AI sentiment analysis tool market is the increasing need for businesses to understand customer sentiment in real-time. Companies are increasingly recognizing the value of listening to and analyzing customer feedback and conversations across various platforms. This is not only improving customer satisfaction but also providing valuable insights that drive product improvements and marketing strategies. Moreover, as businesses strive to become more customer-centric, the demand for advanced tools that can accurately gauge consumer sentiment is witnessing a significant uptick.

Another critical factor contributing to market growth is the integration of natural language processing (NLP) and machine learning (ML) technologies in sentiment analysis tools. These advancements have dramatically improved the accuracy and efficiency of sentiment analysis, enabling businesses to derive actionable insights from vast amounts of unstructured data. The continuous evolution of NLP and ML technologies is likely to further enhance the capabilities of sentiment analysis tools, thus fueling their adoption across various sectors.

Furthermore, the increasing usage of social media platforms and online review sites has provided an abundance of data for sentiment analysis. Companies are leveraging this data to understand public perception, monitor brand reputation, and identify emerging trends. The rise of social media influencers and the growing importance of online reputation management are expected to drive the demand for AI sentiment analysis tools even higher. Additionally, the growing emphasis on personalized customer experiences and targeted marketing campaigns is likely to boost the market growth.

From a regional perspective, North America is expected to dominate the AI sentiment analysis tool market, followed by Europe and Asia-Pacific. The rapid technological advancements, high adoption rate of AI technologies, and the presence of major market players in North America are key factors contributing to this regional dominance. In Europe, stringent data privacy regulations and a strong focus on customer experience are driving the market. Meanwhile, the Asia-Pacific region is anticipated to witness significant growth due to the increasing adoption of AI technologies and the burgeoning e-commerce sector.

Component Analysis

The AI sentiment analysis tool market can be segmented by component into software and services. The software segment holds a significant market share and is expected to continue its dominance throughout the forecast period. The increasing demand for advanced sentiment analysis software that can process large volumes of unstructured data and provide real-time insights is a key driver for this segment. Furthermore, the integration of sophisticated NLP and ML algorithms in sentiment analysis software is enhancing its capabilities, making it more attractive to businesses across various industries.

Another factor contributing to the growth of the software segment is the rising adoption of cloud-based solutions. Cloud-based sentiment analysis software offers several advantages, including scalability, flexibility, and cost-effectiveness. This is particularly beneficial for small and medium enterprises (SMEs) that may not have the resources to invest in expensive on-premises solutions. The increasing preference for cloud-based solutions is expected to drive the growth of the software segment in the coming years.

On the other hand, the services segment, which includes consulting, implementation, and maintenance services, is also expected to witness significant growth. Businesses are increasingly seeking professional services to help them implement and optimize sentiment analysis tools. Consulting services are particularly in demand as organizations look for expert advice on how to best leverage sentiment analysis to achieve their business objectives. Additionally, the need for ongoing maintenance and support services to ensure the smooth functioning of sentiment analysis tools is expected to drive growth in this segme

The Machine Learning in E-commerce Market is estimated to reach USD 98.9 Bn By 2034, Riding on a Strong 36.6% CAGR during forecast period.

Stock price and consumer sentiment consistently serve as pivotal economic indicators for the performance and growth of e-commerce enterprises. It is essential to comprehend and forecast the co-movement between the two to inform financing and investment decision-making effectively. Prior research has focused on predicting individual indicators, but not much of them attempt to forecast their co-movement. We propose a novel Rule Combination based on Bivariate Co-movement Network (RC-BCN) approach for bivariate co-movement forecasting. Bivariate co-movement features extracted utilizing the BCN’s topological nature instruct the entropy optimization in order to enhance the RC-BCN’s predictions. We conduct four sets of experiments on 1,135 data sets from JD.com between 2018 and 2022, where consumer sentiment is measured using text sentiment analysis of online reviews. The results indicate that RC-BCN’s prediction accuracy reaches at most 91% under distortion preference and is improved by 18% compared without entropy optimization. This study highlights the value of complex network and entropy theory in forecasting bivariate co-movement for e-commerce enterprises.

Context

Welcome. This is a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. Its nine supportive features offer a great environment to parse out the text through its multiple dimensions. Because this is real commercial data, it has been anonymized, and references to the company in the review text and body have been replaced with “retailer”.

Content

This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

• Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.
• Age: Positive Integer variable of the reviewers age.
• Title: String variable for the title of the review.
• Review Text: String variable for the review body.
• Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.
• Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.
• Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.
• Division Name: Categorical name of the product high level division.
• Department Name: Categorical name of the product department name.
• Class Name: Categorical name of the product class name.

Acknowledgements

Anonymous but real source

Inspiration

I look forward to come quality NLP! There is also some great opportunities for feature engineering, and multivariate analysis.

Publications

Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network
by Abien Fred Agarap - Github

Global Generative Artificial Intelligence (AI) In E-Commerce market size is expected to reach $2.06 billion by 2029 at 18.6%, segmented as by natural language processing (nlp), chatbots and virtual assistants, sentiment analysis for customer feedback, automated content creation for product descriptions, personalized recommendations and search, language translation services