Search results
Results from the WOW.Com Content Network
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 ...
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.
PolyAnalyst is used in business management to analyze written customer feedback including product review data, warranty claims, and customer comments. [12] In one case, PolyAnalyst was used to build a tool which helped a company monitor its employees' conversations with customers by rating their messages for factors such as professionalism ...
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 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.
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.