Book ChapterDOI
Using pointwise mutual information to identify implicit features in customer reviews
Qi Su,Kun Xiang,Houfeng Wang,Bin Sun,Shiwen Yu +4 more
- pp 22-30
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TLDR
In this article, a polarity lexicon is used to identify implicit product features expressed in product reviews in the context of opinion question answering, and each adjective in the lexicon was mapped to a set of predefined product features.Abstract:
This paper is concerned with automatic identification of implicit product features expressed in product reviews in the context of opinion question answering. Utilizing a polarity lexicon, we map each adjectives in the lexicon to a set of predefined product features. According to the relationship between those opinion-oriented words and product features, we could identify what feature a review is regarding without the appearance of explicit feature nouns or phrases. The results of our experiments proved the validity of this method.read more
Citations
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Journal ArticleDOI
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
TL;DR: This thesis proposes a new senti-lexicon for the sentiment analysis of restaurant reviews using the improved Naive Bayes algorithm, and shows that when this algorithm was used and a unigrams+bigrams was used as the feature, the gap between the positive accuracy and the negative accuracy was narrowed.
Book ChapterDOI
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text
TL;DR: Sentiment analysis is the task of automatically determining from text the attitude, emotion, or some other affectual state of the author as mentioned in this paper, which is a difficult task due to the complexity and subtlety of language use.
BookDOI
A Practical Guide to Sentiment Analysis
TL;DR: The main aim of this book is to provide a feasible research platform to ambitious researchers towards developing the practical solutions that will be indeed beneficial for the authors' society, business and future researches as well.
Proceedings ArticleDOI
Clustering product features for opinion mining
TL;DR: This paper models the sentiment analysis of product reviews problem as a semi-supervised learning problem, and proposes a method to automatically identify some labeled examples that outperforms existing state-of-the-art methods.
Journal ArticleDOI
Sentiment, emotion, purpose, and style in electoral tweets
TL;DR: This work automatically annotates a set of 2012 US presidential election tweets for a number of attributes pertaining to sentiment, emotion, purpose, and style by crowdsourcing, and shows that the tweets convey negative emotions twice as often as positive.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Proceedings ArticleDOI
Mining and summarizing customer reviews
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.
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
Word association norms, mutual information, and lexicography
Kenneth Church,Patrick Hanks +1 more
TL;DR: The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.
Proceedings Article
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: This article proposed an unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended(thumbs down) based on the average semantic orientation of phrases in the review that contain adjectives or adverbs.