Open AccessProceedings Article
Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews
Jianxing Yu,Zheng-Jun Zha,Meng Wang,Kai Wang,Tat-Seng Chua +4 more
- pp 140-150
TLDR
A domain-assisted approach to organize various aspects of a product into a hierarchy by integrating domain knowledge, as well as consumer reviews, and applies the hierarchy to the task of implicit aspect identification.Abstract:
This paper presents a domain-assisted approach to organize various aspects of a product into a hierarchy by integrating domain knowledge (e.g., the product specifications), as well as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of consumer reviews on various product aspects and aggregate consumer opinions on these aspects. With such organization, user can easily grasp the overview of consumer reviews. Furthermore, we apply the hierarchy to the task of implicit aspect identification which aims to infer implicit aspects of the reviews that do not explicitly express those aspects but actually comment on them. The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach.read more
Citations
More filters
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Book
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
TL;DR: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes as discussed by the authors, which offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis.
Journal ArticleDOI
Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu
TL;DR: This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.
Journal ArticleDOI
Aspect extraction in sentiment analysis: comparative analysis and survey
Toqir Ahmad Rana,Yu-N Cheah +1 more
TL;DR: A comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.
Book ChapterDOI
Aspect and Entity Extraction for Opinion Mining
Lei Zhang,Bing Liu +1 more
TL;DR: A broad overview of the tasks and the current state-of-the-art extraction techniques of aspect-based opinion mining is provided.
References
More filters
Book
Opinion Mining and Sentiment Analysis
Bo Pang,Lillian Lee +1 more
TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
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.
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.