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While sentiment analysis has been popular for domains where authors express their opinion rather explicitly ("the movie is awesome"), such as social media and product reviews, only recently robust methods were devised for other domains where sentiment is strongly implicit or indirect.
Similar to text-based sentiment analysis, multimodal sentiment analysis can be applied in the development of different forms of recommender systems such as in the analysis of user-generated videos of movie reviews [6] and general product reviews, [22] to predict the sentiments of customers, and subsequently create product or service ...
Mathematica – provides built in tools for text alignment, pattern matching, clustering and semantic analysis. See Wolfram Language, the programming language of Mathematica. MATLAB offers Text Analytics Toolbox for importing text data, converting it to numeric form for use in machine and deep learning, sentiment analysis and classification ...
Sentiment of each sentence has been hand labeled as positive or negative. 3000 Text Classification, sentiment analysis 2015 [100] [101] D. Kotzias BlogFeedback Dataset Dataset to predict the number of comments a post will receive based on features of that post. Many features of each post extracted. 60,021 Text Regression 2014 [102] [103] K. Buza
Sentiment analysis may involve analysis of products such as movies, books, or hotel reviews for estimating how favorable a review is for the product. [33] Such an analysis may need a labeled data set or labeling of the affectivity of words.
The application of sophisticated linguistic analysis to news and social media has grown from an area of research to mature product solutions since 2007. News analytics and news sentiment calculations are now routinely used by both buy-side and sell-side in alpha generation, trading execution, risk management, and market surveillance and compliance.
Sentiments extracted from the reviews can be seen as users' rating scores on the corresponding features. Popular approaches of opinion-based recommender system utilize various techniques including text mining, information retrieval, sentiment analysis (see also Multimodal sentiment analysis) and deep learning. [59]
He is best known for his research on sentiment analysis (also called opinion mining), fake/deceptive opinion detection, and using association rules for prediction. He also made important contributions to learning from positive and unlabeled examples (or PU learning ), Web data extraction, and interestingness in data mining.