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

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5 last paragraph, there is a positive evaluation about a proposed federal immigration law (“sensible policy”), as well a negative evaluation of the current “failed immigration system”, and a negative evaluation of of Alabama’s law ascribed to “church leaders.” With this information, it’s possible to solve many more complicated sentiment tasks. Consider a particular application where the goal is to determine which political party the author of the editorial aligns himself with. Actors across the political spectrum have varying opinions on both laws in this editorial, so it is not enough to determine that there is positive or negative sentiment in the editorial. Even when combined with topical text classification to determine the subject of the editorial (immigration law), a bag-of-words technique cannot reveal that the negative opinion is about a state immigration law and the positive opinion is about the proposed federal immigration law. If the opinions had been reversed, there would still be positive and negative sentiment in the document, and there would still be topical information about immigration law. Even breaking down the document at the paragraph or sentence level and performing text classification to determine the topic and sentiment of these smaller units of text does not isolate the opinions and topics in a way that clearly correlates opinions with topics. Using structured sentiment information to discover that the negative sentiment is about the Alabama law, and that the positive sentiment is about the federal law does tell us (presuming that we’re versed in United States politics) that the author of this editorial is likely aligned with the Democratic Party. It is also possible to use these structured opinions to separate out opinions about the federal immigration reform, and opinions about the Alabama state law and compare them. Structured sentiment extraction techniques give us the ability to make these kinds of determinations from text.
6 1.2 Structured Opinion Extraction The goal of structured opinion extraction is to extract individual opinions in text and break down those opinions into parts, so that those parts can be used in sentiment analysis applications. To perform structured opinion extraction, there are a number of tasks that one must tackle. First, one must define the scope of the sentiments to be identified, and the structure of the sentiments to identify. Defining the scope of the task can be particularly challenging as one must balance the idea of finding everything that expresses an opinion (no matter how indirectly it does so) with the idea of finding just things that are clearly opinionated and that a lot of people can agree that they understand the opinion the same way. After defining the structured opinion extraction task, one must tackle the technical aspects of the problem. Opinions need to be identified, and ambiguities need to be resolved. The orientation of the opinion (positive or negative) needs to be determined. If they are part of the structure defined for the task, targets (what the opinion is about) and evaluators (the person whose opinion it is) need to be identified and matched up with the opinions that were extracted. There are tradeoffs to be made between identifying all opinions at the cost of finding false positives, or identifying only the opinions that one is confident about at the cost of missing many opinions. Depending on the scope of the opinions, there may be challenges in adapting the technique for use on different genres of text, or developing resources for different genres of text. Lastly, for some domains of text there are more general textprocessing challenges that arise from the style of the text written in the domain. (For example when analyzing Twitter posts, the 140-character length limit for a posting, the informal nature of the medium, and the conventions for hash tags, retweets, and replies can really challenge the text parsers that have been trained on other domains.) The predominant way of thinking about structured opinion extraction in the
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 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: 4 tasks, it is necessary to use str
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
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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
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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
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262 [148] Seki, Y., Ku, L.-W., Sun,
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264 [173] Whitelaw, C., Garg, N., a
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