Sentiment analysis on Chinese web forums using elastic nets: Features, classification and interpretation: Working paper series--11-11

Consumer opinion has always been of great concern for businesses and others in the commercial sector. Among all social media which contain opinion-rich content, Web forums have become influential due to the large volume of discussions and high levels of interactivity. The Chinese market has now emerged as one of the largest ones over the world, therefore understanding the opinions and sentiments expressed by Chinese consumers has become increasingly important. In this study, we proposed a generic framework to analyze sentiment in Chinese Web forums. To detect online sentiment, we developed a classification method using Elastic Nets with rich feature representation. The proposed sentiment analysis framework was evaluated on two of the most famous Chinese forums with topics on Chinese stock market and laptop. Findings about interesting features were discussed

Using syntactic rules to combine opinion elements in Chinese opinion mining systems

[[abstract]]Most Chinese opinion mining systems use the specific pattern and nearby approach to combine relevant opinion elements (feature words and opinion words) to express the opinion tendencies of authors. In this paper, we propose a rule-based ad hoc method to study the combination problem of Chinese opinion elements. We extracted the opinion elements of articles based on lexicons and then combined them with the different sentence patterns and grammars to analyze the authors’ opinions. Because the articles on the online communities such as blogs, wikis, online forums, etc. do not have a defined format, there are often opinion comments that do not refer to the topic, resulting in information loss and significantly reduced recall. Therefore, the “default topic” method is proposed to correct this type of problem. Additionally, there might be errors when using the nearby approach to combine opinion elements. Thus, we propose the concept of “clause priority” to increase precision. After 20 months of long-term tracking and analysis, the experimental result indicates that the method proposed in this paper had good precision, recall, and F1 of opinion tendency analysis for review articles.[[notice]]補正完畢[[journaltype]]國外[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]KO

Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach

Sentiment classification aims at mining reviews of customers for a certain product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers ’ opinions from the web information. Automatic opinion mining will benefit to both consumers and sellers. Up to now, it is still a complicated task with great challenge. There are mainly two types of approaches for sentiment classification, machine learning methods and semantic orientation methods. Though some pioneer researches explored the approaches for English movie review classification, few jobs have been done on sentiment classification for Chinese reviews. The improved semantic approach for sentiment classification on movie reviews written in Chinese was proposed in this paper. Data experiment shows the capability of this approach. Key word

Sentence-level sentiment tagging across different domains and genres

The demand for information about sentiment expressed in texts has stimulated a growing interest into automatic sentiment analysis in Natural Language Processing (NLP). This dissertation is motivated by an unmet need for high-performance domain-independent sentiment taggers and by pressing theoretical questions in NLP, where the exploration of limitations of specific approaches, as well as synergies between them, remain practically unaddressed. This study focuses on sentiment tagging at the sentence level and covers four genres: news, blogs, movie reviews, and product reviews. It draws comparisons between sentiment annotation at different linguistic levels (words, sentences, and texts) and highlights the key differences between supervised machine learning methods that rely on annotated corpora (corpus-based, CBA) and lexicon-based approaches (LBA) to sentiment tagging. Exploring the performance of supervised corpus-based approach to sentiment tagging, this study highlights the strong domain-dependence of the CBA. I present the development of LBA approaches based on general lexicons, such as WordNet, as a potential solution to the domain portability problem. A system for sentiment marker extraction from WordNet's relations and glosses is developed and used to acquire lists for a lexicon-based system for sentiment annotation at the sentence and text levels. It demonstrates that LBA's performance across domains is more stable than that of CBA. Finally, the study proposes an integration of LBA and CBA in an ensemble of classifiers using a precision-based voting technique that allows the ensemble system to incorporate the best features of both CBA and LBA. This combined approach outperforms both base learners and provides a promising solution to the domain-adaptation problem. The study contributes to NLP (1) by developing algorithms for automatic acquisition of sentiment-laden words from dictionary definitions; (2) by conducting a systematic study of approaches to sentiment classification and of factors affecting their performance; (3) by refining the lexicon-based approach by introducing valence shifter handling and parse tree information; and (4) by development of the combined, CBA/LBA approach that brings together the strengths of the two approaches and allows domain-adaptation with limited amounts of labeled training data