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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希(Hsin-Hsi Chen) | |
| dc.contributor.author | Jun-Jie Wang | en |
| dc.contributor.author | 王俊杰 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:35:00Z | - |
| dc.date.available | 2015-07-29 | |
| dc.date.copyright | 2015-07-29 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-07-28 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53968 | - |
| dc.description.abstract | In this “Big Data Era”, mining valuable information from customer reviews can benefit both companies and customers. Sentiment analysis on customer reviews remains to be challenging due to the variety of human language
usages. This thesis proposes a new concept of context in sentiment analysis by exploring real data and referencing works in linguistics. In spite of the popular definition of context which refers to the surroundings of a word and is often used in word sense disambiguation, our context refers to a fragment of texts which only contain ambiguous opinion words or even no opinion words. To conduct the sentiment analysis on customer reviews, a new Chinese customer review dataset in the hotel domain has is on both the snippet level and the clause level. For clause segmentation, two strategies, i.e., punctuation-based method and parsing-based method, have been proposed, and the polarity shift caused by discourse markers and negation operators is also considered in the parsing-based method. To extract the context, two types of the opinion word lexicons are first built and then checked and filtered manually. Thus, four types of lexicons are constructed. We then apply these four lexicons to extracting context on both the snippet level and the context level. In the sentiment classification experiments, Support Vector Machine (SVM) model with bag-of-words features and Convolutional Neuron Network (CNN) model with word embedding vectors are employed. The experimental results show that CNN model can always achieve the highest performance on both levels. Besides, the results also indicate that considering context in the process of sentiment analysis on customer reviews is necessary. In addition, detail error analysis and case study are also performed to understand the experimental results. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:35:00Z (GMT). No. of bitstreams: 1 ntu-104-R02944048-1.pdf: 1168883 bytes, checksum: c0e89072ba2d8f81a328653c522dbeb1 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 口試委員會審定書. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Sentiment Analysis on Customer Reviews . . . . . . . . . . . . . . . . . 1 1.2 Rhetorical Modes in Written Texts . . . . . . . . . . . . . . . . . . . . . 3 1.3 Concept of Context and Research Goal . . . . . . . . . . . . . . . . . . . 6 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Sentiment Analysis in Different Granularities . . . . . . . . . . . . . . . 9 2.1.1 Document-level Sentiment Analysis . . . . . . . . . . . . . . . . 9 2.1.2 Sentence-level Sentiment Analysis . . . . . . . . . . . . . . . . . 10 2.1.3 Word-level Sentiment Analysis . . . . . . . . . . . . . . . . . . 11 2.2 Context Aware Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . 11 2.3 Deep Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1 Word representation . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Sentence Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1 Dataset Selection and Concept Clarification . . . . . . . . . . . . . . . . 19 3.2 Dataset Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 Preprocessing the Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.4 Word Embedding Pre-training Dataset . . . . . . . . . . . . . . . . . . . 28 4 Context Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.1 Clause Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.1.1 Punctuation-based Method . . . . . . . . . . . . . . . . . . . . . 31 4.1.2 Parsing-based Method . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Discourse Marker Process Strategy . . . . . . . . . . . . . . . . 35 4.2 Lexicon Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2.1 Lexicon Construction Methodology . . . . . . . . . . . . . . . . 40 4.2.2 Lexicon Construction Result . . . . . . . . . . . . . . . . . . . . 43 4.3 Context Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Context snippet extraction . . . . . . . . . . . . . . . . . . . . . 45 4.3.2 Context clauses extraction . . . . . . . . . . . . . . . . . . . . . 46 5 Context Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.1 Experiment Design and Method . . . . . . . . . . . . . . . . . . . . . . 49 5.1.1 Experiment Methods . . . . . . . . . . . . . . . . . . . . . . . . 51 5.2 Experimental Result on the Snippet Level . . . . . . . . . . . . . . . . . 53 5.2.1 Experiment on the Complete Snippet . . . . . . . . . . . . . . . 53 5.2.2 Experiment on the Context Snippet . . . . . . . . . . . . . . . . 60 5.3 Experimental Result on the Clause Level . . . . . . . . . . . . . . . . . . 62 5.3.1 Ground Truth Labeling . . . . . . . . . . . . . . . . . . . . . . . 62 5.3.2 Result of Classification on the Clause Level . . . . . . . . . . . . 65 5.3.3 Further Exploration on the CNN Model Issues . . . . . . . . . . 68 5.4 Error Analysis and Case Study . . . . . . . . . . . . . . . . . . . . . . . 71 5.4.1 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.4.2 Case Study of Clause-level Classification Result . . . . . . . . . 78 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 | |
| dc.language.iso | en | |
| dc.subject | 情境相關之情緒分析 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 顧客評論之意見探勘 | zh_TW |
| dc.subject | Context-Aware Sentiment Analysis | en |
| dc.subject | Opinion Mining on Customer Reviews | en |
| dc.subject | Natural Language Processing | en |
| dc.title | 分析顧客評論之情景及其情緒分析之研究 | zh_TW |
| dc.title | Analyzing Context in Customer Reviews for Sentiment
Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳建錦(Chien-Chin Chen),盧文祥(Wen-Hsiang Lu),林川傑(Chuan-Jie Lin) | |
| dc.subject.keyword | 自然語言處理,顧客評論之意見探勘,情境相關之情緒分析, | zh_TW |
| dc.subject.keyword | Natural Language Processing,Opinion Mining on Customer Reviews,Context-Aware Sentiment Analysis, | en |
| dc.relation.page | 98 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-07-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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