Sentiment Analysis based on Appraisal Theory and Functional Local ...

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TABLE OF CONTENTS Page ACKNOWLEDGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . iii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . x LIST OF ALGORITHMS . . . . . . . . . . . . . . . . . . . . . . . . . xii ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii CHAPTER 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . 1 1.1. <strong>Sentiment</strong> Classification versus <strong>Sentiment</strong> Extraction . . . 3 1.2. Structured Opinion Extraction . . . . . . . . . . . . . . 6 1.3. Evaluating Structured Opinion Extraction . . . . . . . . 9 1.4. FLAG: Functional Local Appraisal Grammar Extractor . . 11 1.5. Appraisal Theory in <strong>Sentiment</strong> <strong>Analysis</strong> . . . . . . . . . 14 1.6. Structure of this dissertation . . . . . . . . . . . . . . . 16 2. PRIOR WORK . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1. Applications of <strong>Sentiment</strong> <strong>Analysis</strong> . . . . . . . . . . . . 17 2.2. Evaluation and other kinds of subjectivity . . . . . . . . 18 2.3. Review Classification . . . . . . . . . . . . . . . . . . . 20 2.4. Sentence classification . . . . . . . . . . . . . . . . . . 22 2.5. Structural sentiment extraction techniques . . . . . . . . 25 2.6. Opinion lexicon construction . . . . . . . . . . . . . . . 31 2.7. The grammar of evaluation . . . . . . . . . . . . . . . . 33 2.8. Local Grammars . . . . . . . . . . . . . . . . . . . . . 42 2.9. Barnbrook’s COBUILD Parser . . . . . . . . . . . . . . 47 2.10. FrameNet labeling . . . . . . . . . . . . . . . . . . . . 50 2.11. Information Extraction . . . . . . . . . . . . . . . . . . 51 3. FLAG’S ARCHITECTURE . . . . . . . . . . . . . . . . . . 57 3.1. Architecture Overview . . . . . . . . . . . . . . . . . . 57 3.2. Document Preparation . . . . . . . . . . . . . . . . . . 59 iv
CHAPTER Page 4. THEORETICAL FRAMEWORK . . . . . . . . . . . . . . . 63 4.1. Appraisal Theory . . . . . . . . . . . . . . . . . . . . 63 4.2. Lexicogrammar . . . . . . . . . . . . . . . . . . . . . 71 4.3. Summary . . . . . . . . . . . . . . . . . . . . . . . . 76 5. EVALUATION RESOURCES . . . . . . . . . . . . . . . . . 78 5.1. MPQA 2.0 Corpus . . . . . . . . . . . . . . . . . . . . 79 5.2. UIC Review Corpus . . . . . . . . . . . . . . . . . . . 84 5.3. Darmstadt Service Review Corpus . . . . . . . . . . . . 89 5.4. JDPA <strong>Sentiment</strong> Corpus . . . . . . . . . . . . . . . . . 93 5.5. IIT <strong>Sentiment</strong> Corpus . . . . . . . . . . . . . . . . . . 99 5.6. Summary . . . . . . . . . . . . . . . . . . . . . . . . 105 6. LEXICON-BASED ATTITUDE EXTRACTION . . . . . . . . 106 6.1. Attributes of Attitudes . . . . . . . . . . . . . . . . . . 106 6.2. The FLAG appraisal lexicon . . . . . . . . . . . . . . . 109 6.3. Baseline Lexicons . . . . . . . . . . . . . . . . . . . . 115 6.4. Appraisal Chunking Algorithm . . . . . . . . . . . . . . 116 6.5. Sequence Tagging Baseline . . . . . . . . . . . . . . . . 118 6.6. Summary . . . . . . . . . . . . . . . . . . . . . . . . 122 7. THE LINKAGE EXTRACTOR . . . . . . . . . . . . . . . . 124 7.1. Do All Appraisal Expressions Fit in a Single Sentence? . . 124 7.2. Linkage Specifications . . . . . . . . . . . . . . . . . . 128 7.3. Operation of the Associator . . . . . . . . . . . . . . . 132 7.4. Example of the Associator in Operation . . . . . . . . . 134 7.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . 138 8. LEARNING LINKAGE SPECIFICATIONS . . . . . . . . . . 139 8.1. Hunston and Sinclair’s Linkage Specifications . . . . . . . 139 8.2. Additions to Hunston and Sinclair’s Linkage Specifications 140 8.3. Sorting Linkage Specifications by Specificity . . . . . . . 140 8.4. Finding Linkage Specifications . . . . . . . . . . . . . . 147 8.5. Using Ground Truth Appraisal Expressions as Candidates . 150 8.6. Heuristically Generating Candidates from Unannotated Text 152 8.7. Filtering Candidate Appraisal Expressions . . . . . . . . 153 8.8. Selecting Linkage Specifications by Individual Performance 155 8.9. Selecting Linkage Specifications to Cover the Ground Truth 157 8.10. Summary . . . . . . . . . . . . . . . . . . . . . . . . 157 v
Page 1 and 2: SENTIMENT ANALYSIS BASED ON APPRAIS
Page 3: ACKNOWLEDGMENT I am thankful to God
Page 7 and 8: APPENDIX Page B.5. Evaluator . . .
Page 9 and 10: Table Page 10.14 Performance with t
Page 11 and 12: Figure Page 8.1 “The Matrix is a
Page 13 and 14: ABSTRACT Much of the past work in s
Page 15 and 16: 2 sentiment is to understand where
Page 17 and 18: 4 tasks, it is necessary to use str
Page 19 and 20: 6 1.2 Structured Opinion Extraction
Page 21 and 22: 8 for example. (2) There are a few
Page 23 and 24: 10 evaluated in ways that are suite
Page 25 and 26: 12 appraisal expressions accurately
Page 27 and 28: 14 annotated corpus of opinionated
Page 29 and 30: 16 H7: Restricting linkage specific
Page 31 and 32: 18 as rows and columns as sentiment
Page 33 and 34: 20 2.3 Review Classification One of
Page 35 and 36: 22 use perceptron-based</st
Page 37 and 38: 24 reconciling the differences in t
Page 39 and 40: 26 that describe them. Indeed, when
Page 41 and 42: 28 achieve 0.642 precision and 0.69
Page 43 and 44: 30 word). CFACTS breaks each docume
Page 45 and 46: 32 sets contained seven representat
Page 47 and 48: 34 Sentiment Agree
Page 49 and 50: 36 using the top-level attitude typ
Page 51 and 52: 38 Indexing [42], but rather than u
Page 53 and 54: 40 Core Evaluative Parameters Compr
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42 of evoked appraisal [20, 104, 10
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44 has been marked for the transfor
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46 appear in the COBUILD dictionary
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48 where the head word is located i
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50 which pattern to use to parse a
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52 analysis to link the semantic in
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54 Direct Object: Person] or [Subje
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56 derstanding the specific textual
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58 word’s attribute values from t
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60 The Darmstadt Service Review Cor
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62 easiest nsubj aux prep prep flig
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64 not obviously negative to someon
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66 categories that identify their l
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68 The last major type of attitude
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70 Figure 4.3. The Engagement syste
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72 general concept of each componen
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74 people’s opinions, disagreemen
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76 belongs to the left side of the
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78 CHAPTER 5 EVALUATION RESOURCES T
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80 Sentiment Agree
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82 (which corresponds more-or-less
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84 or sentiment, and whether they a
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86 Tagged features router[+2] setup
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88 Tagged features product[+2][p] r
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90 “I made this mistake” in exa
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92 people, and if you join an Epini
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94 of 180 camera reviews and 462 ca
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96 • attitude Smoked target taill
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98 earlier sentence. Though it is p
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100 have narrative content, and at
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102 quickly. While training this an
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104 her last breath. It appears to
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106 CHAPTER 6 LEXICON-BASED ATTITUD
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108 of the extra choices to be made
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110 Attitude Type Appreciation Comp
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112 attributes. An adjectival appra
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114 Table 6.1. Manually and Automat
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116 word can only appear once in th
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118 ⎡ ⎢ ⎣ Attitude: affect Or
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120 NER BIO models, this is useful
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122 • For each token at position
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124 CHAPTER 7 THE LINKAGE EXTRACTOR
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126 targets. One common way to indi
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128 7.2 Linkage Specifications FLAG
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130 The second part of the linkage
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132 later. When an attitude group m
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134 In the third phase (line 8), th
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136 attitude: type=appreciation The
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138 Priority 1 2 3 Appraisal Expres
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140 that the text of these slots co
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142 Algorithm 8.1 Algorithm for top
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144 particular word in position onl
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146 First linkage specifications 1
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148 a hash map or an array for fast
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150 documents about a single topic,
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152 8.6 Heuristically Generating Ca
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154 • attitude, target, process,
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156 In the first step of the compar
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158 Algorithm 8.3 Covering algorith
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160 Second, it is possible for seve
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162 143, 188]. Although FLAG does n
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164 are out of order. This turns ou
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166 • The preposition connecting
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168 CHAPTER 10 EVALUATION OF PERFOR
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170 of linkage specifications are u
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172 extracted by FLAG that had ambi
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174 as they would be after linking,
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176 Table 10.4. Accuracy of Differe
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178 expressions in a few sentences
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180 (these are discussed in Section
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182 Table 10.5. Performance of Diff
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184 It is worth investigating wheth
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186 10.5 The Document Emphasizing P
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188 Table 10.10. The Effect of Atti
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190 Table 10.13. Performance with t
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192 Table 10.16. Performance with t
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194 Table 10.18. End-to-end Extract
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196 sion extraction, and more resea
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198 for FLAG on each of the testing
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200 0.42 0.4 0.38 0.36 0.34 0.32 0.
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202 majority vote of the different
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204 CHAPTER 11 CONCLUSION 11.1 Appr
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206 In the IIT sentiment corpus, co
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208 The definition of appraisal exp
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210 intended to be a first step in
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212 APPENDIX A READING A SYSTEM DIA
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214 describing the choice to be mad
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216 A.4 Realizations The realizatio
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218 B.1 Introduction We are creatin
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220 tensifier such as “more”. I
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222 as though the polarity word has
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224 R. R. White, and examples of wo
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226 Inscribed appraisal uses explic
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228 (95) So, if you have a attitude
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230 • “ comparator more attitud
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232 If there is a comparator in the
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234 the target-antecedent. The targ
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236 In example 122, we are faced wi
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238 as in example 131. A common sig
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240 (138) target Zack would be eval
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242 In example 143, the evaluator,
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244 In example 151, the possessive
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246 (161) target Today was an attit
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248 Table B.1. How to tag multiple
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250 BIBLIOGRAPHY [1] Akkaya, C., Wi
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252 [26] Bloom, K. and Argamon, S.
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254 [50] Etzioni, O., Banko, M., an
Page 269 and 270:
256 [74] Izard, C. E. (1971). The F
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258 [98] Lexalytics Inc. (2011). So
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260 [124] Mullen, A. and Collier, N
Page 275 and 276:
262 [148] Seki, Y., Ku, L.-W., Sun,
Page 277 and 278:
264 [173] Whitelaw, C., Garg, N., a
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