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

More documents

Recommendations

Info

LIST OF ALGORITHMS Algorithm Page 7.1 Algorithm for turning attitude groups into appraisal expression candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 8.1 Algorithm for topologically sorting linkage specifications . . . . . 142 8.2 Algorithm for learning a linkage specification from a candidate appraisal expression. . . . . . . . . . . . . . . . . . . . . . . . . 149 8.3 Covering algorithm for scoring appraisal expressions . . . . . . . 158 xii
ABSTRACT Much of the past work in structured sentiment extraction has been evaluated in ways that summarize the output of a sentiment extraction technique for a particular application. In order to get a true picture of how accurate a sentiment extraction system is, however, it is important to see how well it performs at finding individual mentions of opinions in a corpus. Past work also focuses heavily on mining opinion/product-feature pairs from product review corpora, which has lead to sentiment extraction systems assuming that the documents they operate on are review-like — that each document concerns only one topic, that there are lots of reviews on a particular product, and that the product features of interest are frequently recurring phrases. Based on existing linguistics research, this dissertation introduces the concept of an appraisal expression, the basic grammatical unit by which an opinion is expressed about a target. The IIT sentiment corpus, intended to present an alternative to both of these assumptions that have pervaded sentiment analysis research, consists of blog posts annotated with appraisal expressions to enable the evaluation of how well sentiment analysis systems find individual appraisal expressions. This dissertation introduces FLAG, an automated system for extracting appraisal expressions. FLAG operates using a three step process: (1) identifying attitude groups using a lexicon-<strong>based</strong> shallow parser, (2) identifying potential structures for the rest of the appraisal expression by identifying patterns in a sentence’s dependency parse tree, (3) selecting the best appraisal expression for each attitude group using a discriminative reranker. FLAG achieves an overall accuracy of 0.261 F 1 at identifying appraisal expressions, which is good considering the difficulty of the task. xiii
Page 1 and 2: SENTIMENT ANALYSIS BASED ON APPRAIS
Page 3 and 4: ACKNOWLEDGMENT I am thankful to God
Page 5 and 6: CHAPTER Page 4. THEORETICAL FRAMEWO
Page 7 and 8: APPENDIX Page B.5. Evaluator . . .
Page 9 and 10: Table Page 10.14 Performance with t
Page 11: Figure Page 8.1 “The Matrix is a
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
Page 55 and 56: 42 of evoked appraisal [20, 104, 10
Page 57 and 58: 44 has been marked for the transfor
Page 59 and 60: 46 appear in the COBUILD dictionary
Page 61 and 62: 48 where the head word is located i
Page 63 and 64:
50 which pattern to use to parse a
Page 65 and 66:
52 analysis to link the semantic in
Page 67 and 68:
54 Direct Object: Person] or [Subje
Page 69 and 70:
56 derstanding the specific textual
Page 71 and 72:
58 word’s attribute values from t
Page 73 and 74:
60 The Darmstadt Service Review Cor
Page 75 and 76:
62 easiest nsubj aux prep prep flig
Page 77 and 78:
64 not obviously negative to someon
Page 79 and 80:
66 categories that identify their l
Page 81 and 82:
68 The last major type of attitude
Page 83 and 84:
70 Figure 4.3. The Engagement syste
Page 85 and 86:
72 general concept of each componen
Page 87 and 88:
74 people’s opinions, disagreemen
Page 89 and 90:
76 belongs to the left side of the
Page 91 and 92:
78 CHAPTER 5 EVALUATION RESOURCES T
Page 93 and 94:
80 Sentiment Agree
Page 95 and 96:
82 (which corresponds more-or-less
Page 97 and 98:
84 or sentiment, and whether they a
Page 99 and 100:
86 Tagged features router[+2] setup
Page 101 and 102:
88 Tagged features product[+2][p] r
Page 103 and 104:
90 “I made this mistake” in exa
Page 105 and 106:
92 people, and if you join an Epini
Page 107 and 108:
94 of 180 camera reviews and 462 ca
Page 109 and 110:
96 • attitude Smoked target taill
Page 111 and 112:
98 earlier sentence. Though it is p
Page 113 and 114:
100 have narrative content, and at
Page 115 and 116:
102 quickly. While training this an
Page 117 and 118:
104 her last breath. It appears to
Page 119 and 120:
106 CHAPTER 6 LEXICON-BASED ATTITUD
Page 121 and 122:
108 of the extra choices to be made
Page 123 and 124:
110 Attitude Type Appreciation Comp
Page 125 and 126:
112 attributes. An adjectival appra
Page 127 and 128:
114 Table 6.1. Manually and Automat
Page 129 and 130:
116 word can only appear once in th
Page 131 and 132:
118 ⎡ ⎢ ⎣ Attitude: affect Or
Page 133 and 134:
120 NER BIO models, this is useful
Page 135 and 136:
122 • For each token at position
Page 137 and 138:
124 CHAPTER 7 THE LINKAGE EXTRACTOR
Page 139 and 140:
126 targets. One common way to indi
Page 141 and 142:
128 7.2 Linkage Specifications FLAG
Page 143 and 144:
130 The second part of the linkage
Page 145 and 146:
132 later. When an attitude group m
Page 147 and 148:
134 In the third phase (line 8), th
Page 149 and 150:
136 attitude: type=appreciation The
Page 151 and 152:
138 Priority 1 2 3 Appraisal Expres
Page 153 and 154:
140 that the text of these slots co
Page 155 and 156:
142 Algorithm 8.1 Algorithm for top
Page 157 and 158:
144 particular word in position onl
Page 159 and 160:
146 First linkage specifications 1
Page 161 and 162:
148 a hash map or an array for fast
Page 163 and 164:
150 documents about a single topic,
Page 165 and 166:
152 8.6 Heuristically Generating Ca
Page 167 and 168:
154 • attitude, target, process,
Page 169 and 170:
156 In the first step of the compar
Page 171 and 172:
158 Algorithm 8.3 Covering algorith
Page 173 and 174:
160 Second, it is possible for seve
Page 175 and 176:
162 143, 188]. Although FLAG does n
Page 177 and 178:
164 are out of order. This turns ou
Page 179 and 180:
166 • The preposition connecting
Page 181 and 182:
168 CHAPTER 10 EVALUATION OF PERFOR
Page 183 and 184:
170 of linkage specifications are u
Page 185 and 186:
172 extracted by FLAG that had ambi
Page 187 and 188:
174 as they would be after linking,
Page 189 and 190:
176 Table 10.4. Accuracy of Differe
Page 191 and 192:
178 expressions in a few sentences
Page 193 and 194:
180 (these are discussed in Section
Page 195 and 196:
182 Table 10.5. Performance of Diff
Page 197 and 198:
184 It is worth investigating wheth
Page 199 and 200:
186 10.5 The Document Emphasizing P
Page 201 and 202:
188 Table 10.10. The Effect of Atti
Page 203 and 204:
190 Table 10.13. Performance with t
Page 205 and 206:
192 Table 10.16. Performance with t
Page 207 and 208:
194 Table 10.18. End-to-end Extract
Page 209 and 210:
196 sion extraction, and more resea
Page 211 and 212:
198 for FLAG on each of the testing
Page 213 and 214:
200 0.42 0.4 0.38 0.36 0.34 0.32 0.
Page 215 and 216:
202 majority vote of the different
Page 217 and 218:
204 CHAPTER 11 CONCLUSION 11.1 Appr
Page 219 and 220:
206 In the IIT sentiment corpus, co
Page 221 and 222:
208 The definition of appraisal exp
Page 223 and 224:
210 intended to be a first step in
Page 225 and 226:
212 APPENDIX A READING A SYSTEM DIA
Page 227 and 228:
214 describing the choice to be mad
Page 229 and 230:
216 A.4 Realizations The realizatio
Page 231 and 232:
218 B.1 Introduction We are creatin
Page 233 and 234:
220 tensifier such as “more”. I
Page 235 and 236:
222 as though the polarity word has
Page 237 and 238:
224 R. R. White, and examples of wo
Page 239 and 240:
226 Inscribed appraisal uses explic
Page 241 and 242:
228 (95) So, if you have a attitude
Page 243 and 244:
230 • “ comparator more attitud
Page 245 and 246:
232 If there is a comparator in the
Page 247 and 248:
234 the target-antecedent. The targ
Page 249 and 250:
236 In example 122, we are faced wi
Page 251 and 252:
238 as in example 131. A common sig
Page 253 and 254:
240 (138) target Zack would be eval
Page 255 and 256:
242 In example 143, the evaluator,
Page 257 and 258:
244 In example 151, the possessive
Page 259 and 260:
246 (161) target Today was an attit
Page 261 and 262:
248 Table B.1. How to tag multiple
Page 263 and 264:
250 BIBLIOGRAPHY [1] Akkaya, C., Wi
Page 265 and 266:
252 [26] Bloom, K. and Argamon, S.
Page 267 and 268:
254 [50] Etzioni, O., Banko, M., an
Page 269 and 270:
256 [74] Izard, C. E. (1971). The F
Page 271 and 272:
258 [98] Lexalytics Inc. (2011). So
Page 273 and 274:
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?