2009 Proceeding- General Chairs:
- Maojin Jiang,
- Bei Yu,
- Program Chair:
- Bei Yu
It is our great pleasure to welcome you to the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement -- TSA'09. This workshop seeks to bring together researchers in both computer science and social sciences who are interested in developing and using topic-sentiment analysis methods to measure mass opinion, and to foster communications between the research community and industry practitioners as well.
The call for papers attracted 21 submissions from 14 countries. The program committee accepted 12 papers that cover a variety of topics, including topic-sentiment modeling, sentiment classification and retrieval, sentiment corpus construction, and applications of topic-sentiment analysis in text summarization, question answering, and recommender systems.
The proposed approaches analyze opinions at all levels of granularity: clause, sub-sentence, sentence, paragraph and document. Most of the approaches combine machine learning and statistical methods and the use of linguistic resources (sentiment lexicons, syntactic rules, etc.) for sentiment identification. User-generated content (UGC) is still the main source of data for topic-sentiment analysis in various domains, like customer reviews, blogs, and discussion boards. In addition to English, corpora of other languages (Chinese, Spanish, and Portuguese) have also been studied. Many authors chose to manually annotate their own sentiment corpora to train machine learning algorithms, or employed automatic methods to acquire the sentiment annotation. This indicates the strong demand for large volume of annotated data in various topics and domains to facilitate topic-sentiment analysis.
Proceeding Downloads
Patterns in the stream: exploring the interaction of polarity, topic, and discourse in a large opinion corpus
A qualitative examination of review texts suggests that there are consistent patterns to how topic and polarity are expressed in discourse. These patterns are visible in the text and paragraph structure, topic depth, and polarity flow. In this paper, we ...
Topic-dependent sentiment analysis of financial blogs
- Neil O'Hare,
- Michael Davy,
- Adam Bermingham,
- Paul Ferguson,
- Páraic Sheridan,
- Cathal Gurrin,
- Alan F. Smeaton
While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment ...
Scary films good, scary flights bad: topic driven feature selection for classification of sentiment
This paper describes preliminary work on feature selection for classification of review text by both sentiment rating and topic. The premise stems from the notion that one size does not fit all; that feature sets for sentiment analysis should be ...
Towards the definition of requirements for mixed fact and opinion question answering systems
The growth of the Social Web led to the birth of new textual genres such as blogs, forums or reviews. Such data sources are extremely relevant because texts pertaining to these categories approach a wide range of topics and are written by people with ...
Automatic creation of a reference corpus for political opinion mining in user-generated content
We propose and evaluate a method for automatically creating a reference corpus for training text classification procedures for mining political opinions in user-generated content. The process starts by compiling a collection of highly opinionated ...
Domain-specific sentiment analysis using contextual feature generation
This paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more ...
Weakly supervised techniques for domain-independent sentiment classification
An important sub-task of sentiment analysis is polarity classification, in which text is classified as being positive or negative. Supervised machine learning techniques can perform this task very effectively. However, they require a large corpus of ...
Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-)
We investigate the accuracy of a set of surface patterns in identifying ironic sentences in comments submitted by users to an on-line newspaper. The initial focus is on identifying irony in sentences containing positive predicates since these sentences ...
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative ...
Aspect-based sentence segmentation for sentiment summarization
Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. ...
Locally contextualized smoothing of language models for sentiment sentence retrieval
Recently, a number of documents are published on the web. One of the crucial techniques to access to such information is sentiment sentence retrieval. It is very useful to retrieve positive or negative opinions to a specific topic at sentence level. ...
Sentiment analysis of movie reviews on discussion boards using a linguistic approach
We propose a linguistic approach for sentiment analysis of message posts on discussion boards. A sentence often contains independent clauses which can represent different opinions on the multiple aspects of a target object. Therefore, the proposed ...
Index Terms
- Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
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