Jin-Cheon Na
ABSTRACT
Several researchers have developed tools for classifying/ clustering Web search results into different topic areas (such as sports, movies, travel, etc.), and to help users identify relevant results quickly in the area of interest. This study follows a similar approach, but is in the area of sentiment classification -- automatically classifying on-line review documents according to the overall sentiment expressed in them. This paper presents a prototype system that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended (or non-recommended) information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents, by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM).
- Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., and Ma, J. Learning to Cluster Web Search Results, In Proceedings of the 27th Annual International ACM SIGIR Conference, Sheffield, South Yorkshire, UK, July 2004, 210--217. Google Scholar
Digital Library
- Palmer, C. R., Pesenti, J., Valdes-Perez, R. E., Christel, M. G., Hauptmann, A. G., Ng, D., and Wactlar, H. D. Demonstration of Hierarchical Document Clustering of Digital Library Retrieval Results, In Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries, June 2001, Roanoke, Virginia, USA, 451. Google ScholarDigital Library
- Na, J.-C., Sui, H., Khoo, C., Chan, S., and Zhou, Y. Effectiveness of Simple Linguistic Processing in Automatic Sentiment Classification of Product Reviews, In Proceedings of the 8th International Society for Knowledge Organization Conference '2004, London, UK, July 2004, 49--54.Google Scholar
- Hatzivassiloglou, V. and Wiebe, J. M. Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In Proceedings of the 17th International Conference on Computational Linguistics, Saarbrücken, Germany, 2000, 299--305. Google ScholarDigital Library
Index Terms
- Sentiment-based search in digital libraries
Recommendations
Sentiment Lexicon Enhanced Neural Sentiment Classification
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge ManagementSentiment classification is an important task in the sentiment analysis field. Many deep learning based sentiment classification methods have been proposed in recent years. However, these methods usually rely on massive labeled texts to train sentiment ...
Sentence-level Sentiment Classification with Weak Supervision
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalSentence-level sentiment classification is important to understand users' fine-grained opinions. Existing methods for sentence-level sentiment classification are mainly based on supervised learning. However, it is difficult to obtain sentiment labels of ...
Sentiment classification of blog posts using topical extracts
ADC '12: Proceedings of the Twenty-Third Australasian Database Conference - Volume 124Unlike news stories and product reviews which usually have a strong focus on a single topic, blog posts are often unstructured, and opinions expressed in blog posts do not necessarily correspond to a specific topic. This can lead to unsatisfactory ...
Comments