A Study on Opinion Analysis and its Applications
Date Issued
2009
Date
2009
Author(s)
Ku, Lun-Wei
Abstract
Opinion analysis contains two main parts: opinion mining and its applications. Opinion mining identifies opinion holders, extracts the relevant opinion sentences and decides their polarity. We first generate a Chinese opinion dictionary NTUSD for mining opinions. Moreover, since there are no commonly applied methods for creating evaluation corpora, we introduce a method for developing reliable opinion corpora involving multiple annotators. e develop algorithms for opinion mining from the macro (un-structural) view and the micro (structural) view. To demonstrate and evaluate the proposed opinion mining algorithms developed from the macro view, news and bloggers’ articles are adopted. Documents in the evaluation corpora are tagged in different units from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve an f-measure 62.16% at the sentence level and 74.37% at the document level.rom the micro view, we further learn the polarity of Chinese words by classifying the word structures. Chinese words are classified into eight types based on the morphological information. Experiments show that the injection of morphological information makes a difference on word polarity identification. Given morphological types of words, the f-score 0.610 is achieved in word polarity prediction without using any word thesauri, which is 8.93% improvement from the f-score 0.56 of bag-of-characters approach. If only words which can bear opinions, i.e., nouns, verbs, adjectives and adverbs are considered, i.e., viewing others as non-opinionated, the word polarity prediction achieves 0.62 when morphological types are employed. With the algorithm from the micro view, the performance achieves 0.77 by incorporating an opinionated word dictionary NTUSD. We extend this idea about relations of characters in words to relations of words in sentences and also achieve a large improvement.everal applications are proposed in this dissertation. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the non-supportive evidence. Opinion tracking monitors the developments of opinions from spatial and temporal dimensions. An opinion tracking is generated to show the variation of opinions.pinion question answering and relationship discovery, are another two applications discussed in more detail. People are interested in not only factual questions, but also opinions. We discuss question analysis and answer passage retrieval in opinion QA systems. For question analysis, six opinion question types are defined. A two-layered framework utilizing two question type classifiers is proposed. The performance achieves 87.8% in general question classification and 92.5% in opinion question classification. For answer passage retrieval, three components are introduced. Relevant sentences retrieved are further identified as to whether the focus (Focus Detection) is in a scope of opinion (Opinion Scope Identification) or not, and, if yes, whether the polarity of the scope and the polarity of the question (Polarity Detection) match with each other. The best model achieves an F-measure of 40.59%. With relevance issues removed, the F-measure of the best model boosts up to 84.96%.bjects which yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. We discover relationships among objects based on their opinion tracking plots and collocations. We collected 1.3M economics-related documents from 93 Web sources over 22 months for experiments, and proposed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best.n achievement of our research is the Chinese opinion analysis system CopeOpi, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. It demonstrates the mentioned approaches and its user interface provides an example for other opinion analysis systems.e extend the research domain to the English language to discuss the research issue in different languages and enlarge the practicability of this research. An English parser is applied to extract structural information of English experiment materials. The discussion of the translation issue on opinion analysis is also included in and some interesting results are reported.
Subjects
Opinion analysis
opinion mining
relationship discovery
opinion question answering
Type
thesis
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