acl acl2011 acl2011-253 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Amitava Das
Abstract: Sentiment analysis is one of the hot demanding research areas since last few decades. Although a formidable amount of research has been done but still the existing reported solutions or available systems are far from perfect or to meet the satisfaction level of end user's. The main issue may be there are many conceptual rules that govern sentiment, and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being. Human psychology directly relates to the unrevealed clues; govern the sentiment realization of us. Human psychology relates many things like social psychology, culture, pragmatics and many more endless intelligent aspects of civilization. Proper incorporation of human psychology into computational sentiment knowledge representation may solve the problem. PsychoSentiWordNet is an extension over SentiWordNet that holds human psychological knowledge and sentiment knowledge simultaneously. 1
Reference: text
sentIndex sentText sentNum sentScore
1 Abstract Sentiment analysis is one of the hot demanding research areas since last few decades. [sent-3, score-0.042]
2 The main issue may be there are many conceptual rules that govern sentiment, and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being. [sent-5, score-0.195]
3 Human psychology directly relates to the unrevealed clues; govern the sentiment realization of us. [sent-6, score-0.757]
4 Human psychology relates many things like social psychology, culture, pragmatics and many more endless intelligent aspects of civilization. [sent-7, score-0.196]
5 Proper incorporation of human psychology into computational sentiment knowledge representation may solve the problem. [sent-8, score-0.563]
6 PsychoSentiWordNet is an extension over SentiWordNet that holds human psychological knowledge and sentiment knowledge simultaneously. [sent-9, score-0.58]
7 1 Introduction In order to identify sentiment from a text, lexical analysis plays a crucial role. [sent-10, score-0.421]
8 For example, words like love, hate, good and favorite directly indicate sentiment or opinion. [sent-11, score-0.421]
9 , 2010) have already proposed techniques for making dictionaries for those sentiment words. [sent-15, score-0.452]
10 But polarity assignment of such sentiment lexicons is a hard semantic disambiguation problem. [sent-16, score-0.602]
11 The regulating aspects 52 which govern the lexical level semantic orientation are natural language context (Pang et al. [sent-17, score-0.33]
12 , 2002), language properties (Wiebe and Mihalcea, 2006), domain pragmatic knowledge (Aue and Gamon, 2005), time dimension (Read, 2005), colors and culture (Strapparava and Ozbal, 2010) and many more unrevealed hidden aspects. [sent-18, score-0.136]
13 What previous studies proposed is to attach prior polarity to each sentiment lexicon level. [sent-20, score-0.689]
14 Prior polarity is an approximation value based on corpus heuristics based statistics and not exact. [sent-21, score-0.181]
15 The probabilistic fixed point prior polarity scores do not solve the problem completely rather it shoves the problem into next level, called contextual polarity classification. [sent-22, score-0.414]
16 The hypothesis we started with is that the summation of all the regulating aspects of sentiment orientation is human psychology and thus it is called multi-faceted problem (Liu, 2010). [sent-23, score-0.79]
17 More precisely what we meant by human psychology is the all known and unknown aspects, directly or indirectly govern the sentiment orientation knowledge of us. [sent-24, score-0.729]
18 The regulating aspects wrapped in the present PsychoSentiWordNet are Gender, Age, City, Country, Language and Profession. [sent-25, score-0.164]
19 The PsychoSentiWordNet is an extension over the existing SentiWordNet to hold the possible psychological ingredients, governs the sentiment understandability of us. [sent-26, score-0.549]
20 The PsychoSentiWordNet holds variable prior polarity scores, could be fetched depending upon those psychological regulating aspects. [sent-27, score-0.435]
21 An example may illustrate the definition better for the concept “Rock_Climbing”: Aspects (Age) Polarity - -- - - -- - - -- - - -- - - -- - - -- - - -- - - - ----- --- ---- Null Positive 50-54 Negative 26-29 Positive Portland, OPR,ro UcSeeAdi 1n9g-s2 o4f J uthnee A 2C01L-1H. [sent-28, score-0.041]
22 c T2 2001111 A Sstsuodceinatti Soens fsoiorn C,o pmagpeusta 52ti–o5n7a,l Linguistics In the previous example the described concept “Rock_Climbing” is generally positive as it is adventurous and people have it to make fun or excursion. [sent-30, score-0.216]
23 But it demands highly physical ability thus may be not as good for aged people like the younger people. [sent-31, score-0.113]
24 In this paper, we propose an interactive gaming (Dr Sentiment) technology to collect psycho-sentimental polarity for lexicons. [sent-36, score-0.315]
25 Section 3 explains about some exciting outcomes that support the usefulness of the PsychoSentiWordNet. [sent-39, score-0.071]
26 What we believe is the developed PsychoSentiWordNet will help automatic sentiment analysis research in many aspect and other disciplines as well, described in the section 4. [sent-40, score-0.421]
27 2 Dr Sentiment Dr Sentiment 1 is a template based interactive online game, which collects player’s sentiment by asking a set of simple template based questions and finally reveals a player’s sentimental status. [sent-42, score-0.519]
28 Dr Sentiment fetches random words from SentiWordNet synsets and asks every player to tell about his/her sentiment polarity understanding regarding the concept behind. [sent-43, score-0.953]
29 There are several motivations behind developing an intuitive game to automatically collect human psycho-sentimental orientation information. [sent-44, score-0.349]
30 , 2004) innovate the concept of a game to automatically label images available in the World Wide Web. [sent-46, score-0.3]
31 It has been identified as the most reliable strategy to automatically annotate the online images. [sent-47, score-0.043]
32 These techniques can be broadly categorized in two genres, one follows classical manual annotation (Andreevskaia and Bergler, 2006);(Wiebe and Riloff, 2006); (Mohammad et al. [sent-54, score-0.06]
33 , 2008) techniques and the others proposed various automatic techniques (Tong, 2001). [sent-55, score-0.062]
34 Manual annotation techniques are undoubtedly trustable but it generally takes time. [sent-57, score-0.08]
35 Automatic techniques demands manual validations and are dependent on the corpus availability in the respective domain. [sent-58, score-0.102]
36 Manual annotation technique required a large number of annotators to balance one’s sentimentality in order to reach agreement. [sent-59, score-0.168]
37 But sentiment is a property of human intelligence and is not entirely based on the features of a language. [sent-61, score-0.452]
38 Thus people’s involvement is required to capture the sentiment of the human society. [sent-62, score-0.504]
39 We have developed an online game to attract internet population for the creation of PsychoSentiWordNet automatically. [sent-63, score-0.303]
40 Involvement of Internet population is an effective approach as the population is very high in number and ever growing (approx. [sent-64, score-0.132]
41 Internet population consists of people with various languages, cultures, age etc and thus not biased towards any domain, language or particular society. [sent-66, score-0.197]
42 The Sign Up form of the “Dr Sentiment” game asks the player to provide personal information such as Sex, Age, City, Country, Language and Profession. [sent-67, score-0.457]
43 The lexicons tagged by this system are credible as it is tagged by human beings. [sent-68, score-0.263]
44 In either way it is not like a static sentiment lexicon set as it is updated regularly. [sent-69, score-0.492]
45 Almost 100 players per day are currently playing it throughout the world in different languages. [sent-70, score-0.189]
46 To make the gaming interface more interesting images has been added with the help of Google image search API and to avoid biasness we have randomized among the first ten images retrieved by Google. [sent-73, score-0.432]
47 Snapshots of different screens from the game are presented in Figure 1. [sent-74, score-0.184]
48 There are predefined distributions of each question type as 11 for Q1, 11 for Q2, 4 for Q3 and 4 for Q4. [sent-83, score-0.066]
49 The questions are randomly asked to keep the game more interesting. [sent-85, score-0.324]
50 The Google image search API is fired with the word as a query. [sent-88, score-0.059]
51 An image along with the word itself is shown in the Q1 page of the game. [sent-89, score-0.059]
52 Players press the different emoticons (Fig 2) to express their sentimentality. [sent-90, score-0.056]
53 3 Q2 This question type is specially designed for relative scoring technique. [sent-93, score-0.066]
54 For example: good and better both are positive but we need to know which one is 54 more positive than other. [sent-94, score-0.166]
55 With the present gaming technology relative polarity scoring has been assigned to each n-n word pair combination. [sent-96, score-0.284]
56 Randomly n (presently 2-4) words have been chosen from the source SentiWordNet synsets along with their images as retrieved by Google API. [sent-98, score-0.173]
57 Each player is then asked to select one of them that he/she likes most. [sent-99, score-0.254]
58 The relative score is calculated and stored in the corresponding log log table. [sent-100, score-0.08]
59 4 Q3 The player is asked for any positive word in his/her mind. [sent-104, score-0.337]
60 The word is then added to the PsychoSentiWordNet and further used in Q1 to other users to note their sentimentality about the particular word. [sent-106, score-0.168]
61 5 Q4 A player is asked by Dr Sentiment about any negative word. [sent-108, score-0.32]
62 The word is then added to the PsychoSentiWordNet and further used in Q1 to other users to note their sentimentality about the particular word. [sent-109, score-0.168]
63 6 Comment Architecture There are three types of Comments, Comment type 1 (CMNT1), Comment type 2 (CMNT2) and the final comment as Dr Sentiment’s prescription. [sent-111, score-0.215]
64 CMNT1 type and CMNT2 type comments are associated with question types Q1 and Q2 respectively. [sent-112, score-0.141]
65 7 CMNT1 Comment type 1 has 5 variations as shown in the Comment table in Table 3. [sent-114, score-0.067]
66 Comments are randomly retrieved from comment type table according to their category. [sent-115, score-0.269]
67 • Positive word has been tagged as negative (PN) • • • • 2. [sent-116, score-0.182]
68 8 Positive word has been tagged as positive (PP) Negative word has been tagged as positive (NP) Negative word has been tagged as negative (NN) Neutral (NU) CMNT2 The strategy here is as same as the CMNT 1. [sent-117, score-0.623]
69 (PN) • Negative word has been tagged as positive (NP) 2. [sent-120, score-0.199]
70 9 Dr Sentiment’s Prescription The final prescription depends on various factors such as total number of positive, negative or neutral comments and the total time taken by any player. [sent-121, score-0.237]
71 The final prescription also depends on the range of the values of accumulating all the above factors. [sent-122, score-0.078]
72 The provoking message for players is Dr Sentiment can reveal their sentimental status: whether they are extreme negative or positive or very much neutral or diplomatic etc. [sent-124, score-0.412]
73 A word previously tagged by a player is avoided by the tracking system for the next time playing as our intension is to tag more and more words involving Internet population. [sent-125, score-0.397]
74 We observe that the strategy helps to keep the game interesting as a large number of players return to play the game after this strategy was implemented. [sent-126, score-0.664]
75 We are not demanding that the revealed status of a player by Dr Sentiment is exact or ideal. [sent-127, score-0.283]
76 It is only to make fun but the outcomes of the game 55 effectively help to store human sentimental psychology in terms of computational lexicon. [sent-128, score-0.476]
77 3 Senti-Mentality PsychoSentiWordNet gives a good sketch to understand the psycho-sentimental behavior of society depending upon proposed psychological dimensions. [sent-129, score-0.09]
78 The PsychoSentiWordNet is basically the log records of every player’ s tagged words. [sent-130, score-0.189]
79 1 Concept-Culture-Wise Analysis Figure 3: Geospatial Senti-Mentality The word “blue” get tagged by different players around the world. [sent-132, score-0.265]
80 But surprisingly it has been tagged as positive from one part of the world and negative from another part of the world. [sent-133, score-0.265]
81 The observation is that most of the negative tags are coming from the middle-east and especially from the Islamic countries. [sent-135, score-0.066]
82 This information could be further retrieved from the developed source by giving information like (blue, Italy), (blue, Iraq) or (blue, USA) etc. [sent-139, score-0.061]
83 2 Age-Wise Analysis Another interesting observation is that sentimentality may vary age-wise. [sent-141, score-0.198]
84 The total number of players for each range of age is shown at top of every bar. [sent-146, score-0.237]
85 In the Figure 4 the horizontal bars are divided into two colors (Green depicts the Positivity and Red depicts the negativity) according to the total positivity and negativity scores, gathered during playing. [sent-147, score-0.237]
86 This sociological study gives an idea that variation of sentimentality with age. [sent-148, score-0.168]
87 This information could be further retrieved from the developed source by giving information like (X, 36-39) or (X, 45-49) etc. [sent-149, score-0.061]
88 3 Gender Specific It is observed from the statistics collected that women are more positive than a man. [sent-151, score-0.129]
89 The variations in sentimentality among men and women are shown in the following Figure 5. [sent-152, score-0.245]
90 Studies on the combinations of the proposed psychological dimensions, such as, location-age, location56 profession and gender-location may reveal some interesting results. [sent-155, score-0.12]
91 Moreover the other non linguistic psychological dimensions are very much important for further analysis and in several newly discovered sub-disciplines such as: Geospatial Information retrieval (Egenhofer, 2002), Personalized search (Gaucha et al. [sent-157, score-0.119]
92 Several tables are being used to keep user’s clicking log and their personal information. [sent-161, score-0.071]
93 As one of the research motivations was to generate up-to-date prior polarity scores thus we decided to generate web service API by that people could access latest prior polarity scores. [sent-162, score-0.549]
94 We do believe this method will over perform than a static sentiment lexicon set. [sent-163, score-0.492]
95 No evaluation has been done yet as there is no data available for this kind of experimentation and to the best of our knowledge this is the first endeavor where sentiment meets psychology. [sent-165, score-0.421]
96 Our present goal is to collect such corpus and experiment to check whether variable prior polarity score of PsychoSentiWordNet excel over the fixed point prior polarity score of SentiWordNet. [sent-166, score-0.525]
97 CLaC and CLaC-NB: Knowledge-based and corpus-based approaches to sentiment tagging. [sent-169, score-0.421]
98 , Customizing sentiment classifiers to new domains: A case study. [sent-178, score-0.421]
99 Using emoticons to reduce dependency in machine learning techniques for sentiment classification. [sent-199, score-0.508]
100 An operational system for detecting and tracking opinions in online discussion. [sent-217, score-0.081]
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Typical supervised learning approaches to sentence-level sentiment analysis rely on sentence-level supervision. While such fine-grained supervision rarely exist naturally, and thus requires labor intensive manual annotation effort (Wiebe et al., 2005), coarse-grained supervision is naturally abundant in the form of online review ratings. This coarse-grained supervision is, of course, less informative compared to fine-grained supervision, however, by combining a small amount of sentence-level supervision with a large amount of document-level supervision, we are able to substantially improve on the sentence-level classification task. Our work combines two strands of research: models for sentiment analysis that take document structure into account; – 569 Ryan McDonald Google, Inc., New York ryanmcd@ google com . and models that use latent variables to learn unobserved phenomena from that which can be observed. Exploiting document structure for sentiment analysis has attracted research attention since the early work of Pang and Lee (2004), who performed minimal cuts in a sentence graph to select subjective sentences. McDonald et al. (2007) later showed that jointly learning fine-grained (sentence) and coarsegrained (document) sentiment improves predictions at both levels. More recently, Yessenalina et al. (2010) described how sentence-level latent variables can be used to improve document-level prediction and Nakagawa et al. (2010) used latent variables over syntactic dependency trees to improve sentence-level prediction, using only labeled sentences for training. In a similar vein, Sauper et al. (2010) integrated generative content structure models with discriminative models for multi-aspect sentiment summarization and ranking. These approaches all rely on the availability of fine-grained annotations, but Ta¨ckstro¨m and McDonald (201 1) showed that latent variables can be used to learn fine-grained sentiment using only coarse-grained supervision. While this model was shown to beat a set of natural baselines with quite a wide margin, it has its shortcomings. Most notably, due to the loose constraints provided by the coarse supervision, it tends to only predict the two dominant fine-grained sentiment categories well for each document sentiment category, so that almost all sentences in positive documents are deemed positive or neutral, and vice versa for negative documents. As a way of overcoming these shortcomings, we propose to fuse a coarsely supervised model with a fully supervised model. Below, we describe two ways of achieving such a combined model in the framework of structured conditional latent variable models. Contrary to (generative) topic models (Mei et al., 2007; Titov and Proceedings ofP thoer t4l9atnhd A, Onrnuegaoln M,e Jeuntineg 19 o-f2 t4h,e 2 A0s1s1o.c?i ac t2io0n11 fo Ar Cssoocmiaptuiotanti foonra Clo Lminpguutiast i ocns:aslh Loirntpgaupisetrics , pages 569–574, Figure 1: a) Factor graph of the fully observed graphical model. b) Factor graph of the corresponding latent variable model. During training, shaded nodes are observed, while non-shaded nodes are unobserved. The input sentences si are always observed. Note that there are no factors connecting the document node, yd, with the input nodes, s, so that the sentence-level variables, ys, in effect form a bottleneck between the document sentiment and the input sentences. McDonald, 2008; Lin and He, 2009), structured conditional models can handle rich and overlapping features and allow for exact inference and simple gradient based estimation. The former models are largely orthogonal to the one we propose in this work and combining their merits might be fruitful. As shown by Sauper et al. (2010), it is possible to fuse generative document structure models and task specific structured conditional models. While we do model document structure in terms of sentiment transitions, we do not model topical structure. An interesting avenue for future work would be to extend the model of Sauper et al. (2010) to take coarse-grained taskspecific supervision into account, while modeling fine-grained task-specific aspects with latent variables. Note also that the proposed approach is orthogonal to semi-supervised and unsupervised induction of context independent (prior polarity) lexicons (Turney, 2002; Kim and Hovy, 2004; Esuli and Sebastiani, 2009; Rao and Ravichandran, 2009; Velikovich et al., 2010). The output of such models could readily be incorporated as features in the proposed model. 1.1 Preliminaries Let d be a document consisting of n sentences, s = (si)in=1, with a document–sentence-sequence pair denoted d = (d, s). Let yd = (yd, ys) denote random variables1 the document level sentiment, yd, and the sequence of sentence level sentiment, = (ysi)in=1 . – ys 1We are abusing notation throughout by using the same symbols to refer to random variables and their particular assignments. 570 In what follows, we assume that we have access to two training sets: a small set of fully labeled instances, DF = {(dj, and a large set of ydj)}jm=f1, coarsely labeled instances DC = {(dj, yjd)}jm=fm+fm+c1. Furthermore, we assume that yd and all yis take values in {POS, NEG, NEU}. We focus on structured conditional models in the exponential family, with the standard parametrization pθ(yd,ys|s) = expnhφ(yd,ys,s),θi − Aθ(s)o
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