Proceedings ArticleDOI
An artificial neural network based approach for sentiment analysis of opinionated text
Anuj Sharma,Shubhamoy Dey +1 more
- pp 37-42
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TLDR
A sentiment classification model using back-propagation artificial neural network (BPANN) is proposed that combines the strength of BPANN in classification accuracy with utilizing intrinsic domain knowledge available in the sentiment lexicons.Abstract:
The Internet and Web 2.0 social media have emerged as an important medium for expressing sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining is becoming an open research domain due to the abundance of discussion forums, Weblogs, e-commerce portals, social networking and content sharing sites where people tend to express their opinions. Sentiment Analysis involves classifying text documents based on the opinion expressed being positive or negative about a given topic. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with utilizing intrinsic domain knowledge available in the sentiment lexicons. The results obtained on the movie-review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate sentiment based classification of text.read more
Citations
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Analisis Sentimen Terhadap Review Film Menggunakan MetodeModified Balanced Random Forest dan Mutual Information
TL;DR: Sentiment analysis can handle sentiment analysis, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system.
Comparative Study among Approaches based in Fuzzy Systems and Artificial Neural Networks to Estimate Importance of Comments about Products and Services
TL;DR: This work proposes adaptations in two input variables, namely: quantity of tuples and vocabulary richness and the building of new approaches using computational models based on Fuzzy Systems and Artificial Neural Networks (ANN).
Proceedings ArticleDOI
Integrating Labeled Latent Dirichlet Allocation into sentiment analysis of movie and general domains
TL;DR: The work proposes to integrate different preprocessing methods including Labeled Latent Dirichlet Allocation, removing stop words and using adjectives that have a significant impact on the document's sentiment, into three popular text classification algorithms: Support Vector Machine, Naïve Bayes and artificial neural network.
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Airline tweets sentimental analysis using Adaptive rider optimization based support vector neural network
S.Sathish Kumar,Aruchamy Rajini +1 more
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Detection of Opinion Communities with the Help of Chance-Corrected Measures of Agreement
Anton Oleinik,Anton Oleinik +1 more
TL;DR: This paper discusses the feasibility and benefits of incorporating coefficients of inter-coder agreement (Krippendorff's α, Bennett, Alpert and Goldstein’s S, Scott's π and Cohen's κ) into recommender systems and argues that with their help it is possible to increase the accuracy of users' assessment of various items.
References
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Proceedings ArticleDOI
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Minqing Hu,Bing Liu +1 more
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Thumbs up? Sentiment Classiflcation using Machine Learning Techniques
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Posted Content
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Journal ArticleDOI
The Effect of Word of Mouth on Sales: Online Book Reviews
Judith A. Chevalier,Dina Mayzlin +1 more
TL;DR: The authors examine the effect of consumer reviews on relative sales of books at Amazon.com and Barnesandnoble.com, and find that reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon and that an improvement in a book's reviews leads to an increase in relative sales.
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