emnlp emnlp2011 emnlp2011-30 knowledge-graph by maker-knowledge-mining

30 emnlp-2011-Compositional Matrix-Space Models for Sentiment Analysis

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Author: Ainur Yessenalina ; Claire Cardie

Abstract: We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accurate assignment of phrase-level sentiment. For example, combining an adverb (e.g., “very”) with a positive polar adjective (e.g., “good”) produces a phrase (“very good”) with increased polarity over the adjective alone. Inspired by recent work on distributional approaches to compositionality, we model each word as a matrix and combine words using iterated matrix multiplication, which allows for the modeling of both additive and multiplicative semantic effects. Although the multiplication-based matrix-space framework has been shown to be a theoretically elegant way to model composition (Rudolph and Giesbrecht, 2010), training such models has to be done carefully: the optimization is nonconvex and requires a good initial starting point. This paper presents the first such algorithm for learning a matrix-space model for semantic composition. In the context of the phrase-level sentiment analysis task, our experimental results show statistically significant improvements in performance over a bagof-words model.

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Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 edu l Abstract We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. [sent-4, score-1.451]

2 Thus, we can model the compositional effects required for accurate assignment of phrase-level sentiment. [sent-5, score-0.245]

3 , “good”) produces a phrase (“very good”) with increased polarity over the adjective alone. [sent-10, score-0.31]

4 Inspired by recent work on distributional approaches to compositionality, we model each word as a matrix and combine words using iterated matrix multiplication, which allows for the modeling of both additive and multiplicative semantic effects. [sent-11, score-0.426]

5 Although the multiplication-based matrix-space framework has been shown to be a theoretically elegant way to model composition (Rudolph and Giesbrecht, 2010), training such models has to be done carefully: the optimization is nonconvex and requires a good initial starting point. [sent-12, score-0.231]

6 In the context of the phrase-level sentiment analysis task, our experimental results show statistically significant improvements in performance over a bagof-words model. [sent-14, score-0.507]

7 Work in the area ranges from identifying the sentiment of individual words to determining the sentiment of phrases, sentences and doc172 Claire Cardie Dept. [sent-16, score-1.014]

8 negative sentiment, collapsing positive (or negative) words, phrases and documents of differing intensities into just one positive (or negative) class. [sent-21, score-0.251]

9 For word-level sentiment, therefore, these methods would not recognize a difference in sentiment between words like “good” and “great”, which have the same direction of polarity (i. [sent-22, score-0.673]

10 At the phrase level, the methods will fail to register compositional effects in sentiment brought about by intensifiers like “very”, “absolutely”, “extremely”, etc. [sent-25, score-0.84]

11 In real-world settings, on the other hand, sentiment values extend across a polarity spectrum from very negative, to neutral, to very positive. [sent-27, score-0.673]

12 — This paper describes a general approach for phrase-level sentiment analysis that takes these realworld requirements into account: we adopt a fivelevel ordinal sentiment scale and present a learningbased method that assigns ordinal sentiment scores to phrases. [sent-30, score-1.992]

13 Consider, for example, combining an adverb like “very” with a polar adjective like “good”. [sent-35, score-0.21]

14 Combining “very” with a negative adjective, like “bad”, produces a phrase (“very bad”) that should be characterized as more negative than the original adjective. [sent-37, score-0.24]

15 Thus, it is convenient to think of the effect of combining an intensifying adverb with a polar adjective as being multiplicative in nature, if we assume the adjectives (“good” and “bad”) to have positive and a negative sentiment scores, respectively. [sent-38, score-1.0]

16 When modeling only positive and negative labels for sentiment, negators are generally treated as flipping the polarity of the adjective it modifies (Choi and Cardie, 2008; Nakagawa et al. [sent-40, score-0.507]

17 , 2011; Liu and Seneff, 2009) suggests that the effect of the negator when ordinal sentiment scores are employed is more akin to dampening the adjective’s polarity rather than flipping it. [sent-43, score-0.977]

18 For these cases, it is convenient to view “not” as shifting polarity to the opposite side of polarity scale by some value. [sent-46, score-0.368]

19 There are, of course, more interesting examples of compositional semantic effects on sentiment: e. [sent-47, score-0.245]

20 Here, the verbs prevent and ease act as content-word negators (Choi and Cardie, 2008) in that they modify the negative sentiment of their direct object arguments so that the phrase as a whole is perceived as somewhat positive. [sent-50, score-0.761]

21 173 One notable exception is Moilanen and Pulman (2007), who propose a compositional semantic approach to assign a positive or negative sentiment to newspaper article titles. [sent-56, score-0.853]

22 However, their knowledgebased approach presupposes the existence of a sentiment lexicon and a set of symbolic compositional rules. [sent-57, score-0.708]

23 But learning-based compositional approaches for sentiment analyis also exist. [sent-58, score-0.708]

24 Choi and Cardie (2008), for example, propose an algorithm for phrase-based sentiment analysis that learns proper assignments of intermediate sentiment analysis decision variables given the a priori (i. [sent-59, score-1.014]

25 , out of context) polarity of the words in the phrase and the (correct) phrase-level polarity. [sent-61, score-0.224]

26 As in Moilianen and Pulman (2007), semantic inference is based on (a small set of) hand-written compositional rules. [sent-62, score-0.201]

27 al (2010) use a dependency parse tree to guide the learning of compositional effects. [sent-64, score-0.201]

28 Each of the above, however, uses a binary rather than an ordinal sentiment scale. [sent-65, score-0.707]

29 In contrast, our proposed method for phraselevel sentiment analysis is inspired by recent work on distributional approaches to compositionality. [sent-66, score-0.548]

30 In particular, Baroni and Zamparelli (2010) tackle adjective-noun compositions using a vector representation for nouns and learning a matrix representation for each adjective. [sent-67, score-0.177]

31 The adjective matrices are then applied as functions over the meanings ofnouns via matrix-vector multiplication to derive the meaning of adjective-noun combinations. [sent-68, score-0.299]

32 In the sections below, we propose a learningbased approach to assign ordinal sentiment scores to sentiment-bearing phrases using a general compositional matrix-space model of language. [sent-71, score-0.943]

33 In contrast to previous work, all words are modeled as matrices, independent of their part-of-speech, and compositional inference is uniformly modeled as matrix multiplication. [sent-72, score-0.337]

34 To predict an ordinal scale sentiment value, we employ Ordered Logistic Regression, introducing a novel training algorithm to accommodate our compositional matrix-space representations (Section 2). [sent-73, score-0.944]

35 We evaluate the approach on a standard sentiment corpus (Wiebe et al. [sent-75, score-0.507]

36 We show (Section 4) that our matrix-space model significantly outperforms a bag-of-words model for the ordinal scale sentiment prediction task. [sent-77, score-0.743]

37 2 The Model for Ordinal Scale Sentiment Prediction As described above, our task is to predict an ordinal scale sentiment value for a phrase. [sent-78, score-0.743]

38 To this end, we employ a sentiment scale with five ordinal values: VERY NEGATIVE, NEGATIVE, NEUTRAL, POSITIVE and VERY POSITIVE. [sent-79, score-0.743]

39 Given a set of phraselevel training examples with their gold-standard ordinal sentiment value, we then use an Ordered Logistic Regression (OLogReg) model for prediction. [sent-80, score-0.748]

40 In the next subsections, we instantiate OLogReg for our sentiment prediction task using a matrix-space word model (2. [sent-86, score-0.507]

41 Let xi be the i-th phrase and yi would be the label of xi, where yi takes r different values yi ∈ {0, . [sent-96, score-0.387]

42 Note, that unlike bag-of-words model, the matrix-space model takes word order into account, since matrix multiplication is not commutative operation. [sent-121, score-0.203]

43 Then the constraints (3) would be: κr−1 ≥ τj 0, 1 ≤ j ≤ r − 2 (4) To simplify the equations we can rewrite the negative loglikelihood as follows: L = −Xi=n1Xkr−=10ln(Aik− Bik)I(yi= k) (5) where Aik=(1F,(κ0+Pjk=1τj− ξi), if k = 0,. [sent-138, score-0.179]

44 m affine (7) We choose the class of affine matrices since for affine matrices matrix multiplication represents both operations: linear transformation and translation. [sent-150, score-1.152]

45 Linear transformation is important for modeling changes in sentiment - translation is also useful (we make use of a translation vector during initialization, see Section 2. [sent-151, score-0.591]

46 Applying the affine transformation W to vector [x, 1]T is equivalent to applying linear transformation A and translation b to x. [sent-156, score-0.359]

47 Also the product of affine matrices is an affine matrix. [sent-162, score-0.528]

48 In case if a certain word appears multiple times in the phrase, the derivative with respect to that word would be a sum of derivatives with respect to each appearance of a word, while all other appearances are fixed. [sent-190, score-0.208]

49 So, given the negative loglikelihood and the derivatives with respect κ0 and τj-s and word matrices W, we optimize objective (5) subject to τj ≥ 0. [sent-212, score-0.405]

50 Fi∈rst mRake the matrix affine by updating the la∈st R row, rthsten m tahkee updated rmixatr afixfi nweil bl ylo uokpd laitkien:g Wˆj = ? [sent-222, score-0.327]

51 It can be proven that such a projection returns the closest affine matrix in Frobenius norm. [sent-224, score-0.327]

52 More specifically, we first use the Singular Value Decomposition (SVD) of the A11: UΣVT = A11, where U and V are orthogonal matrices, Σ is a matrix with singular values on the diagonal. [sent-228, score-0.18]

53 L-BFGS-B returns a solution that is not necessarily an affine matrix. [sent-240, score-0.191]

54 After projecting to the space of affine matrices we start L-BFGS-B from a better initial point. [sent-241, score-0.337]

55 We minimize negative loglikelihood using L-BFGS-B subject to τj ≥ 0. [sent-255, score-0.179]

56 The L2-regularized negative loglikelihood will consist of the expression in (5) and an additional term | |w| |22, where | | · ||2 is the L2-norm of a vector. [sent-258, score-0.212]

57 T|h|ew |d|er,i wvahtievree |o|f ·th ||e additional term with respect to w will be: 2λ ∂λ2∂||ww||22= λw Hence the partial derivative with respect to wxij will have an additional term λwxji. [sent-259, score-0.219]

58 1 applying transformation A of affine matrix W can model a linear transformation, while vector b represents a translation. [sent-272, score-0.411]

59 Since matrix-space model can encode a vectorspace model (Rudolph and Giesbrecht, 2010), we can initialize the matrices to exactly mimic the bagof-words model. [sent-273, score-0.2]

60 a phrase x1, x2 weights of these want to have the Here we assume x2 To compute the polarity score of the bag-of-words model sums the two words: wx1 and wx2 . [sent-276, score-0.224]

61 edu/mpqa/ 178 npP oe lsugait ritav ley hI mniget hd ni,su e imx tyre m e ,mdiuOlrad3b0241ienla Table 1: Mapping of combination of polarities and intensities from MPQA dataset to our ordinal sentiment scale. [sent-291, score-0.759]

62 7 The schematic mapping of phrase polarity and intensity values on ordinal sentimental scale is shown in Table 1. [sent-293, score-0.495]

63 With this value of λ fixed, the final model is the one with the lowest negative loglikelihood on the training set. [sent-309, score-0.216]

64 In Table 3 we show the sentiment scores of the best performing bag-of-words OLogReg model and the best performing model based 179 Table 3: Phrase and the sentiment scores of the phrase for 2 models Matrix-space OLogReg+BowInit and Bag-ofwords OLogReg respectively. [sent-345, score-1.072]

65 By sentiment score, we mean equation (1) of Bag-ofwords OLogReg and equation (2) of Matrix-space OLogReg+BowInit. [sent-347, score-0.507]

66 Here we choose two popular adjectives like ‘good’ and ‘bad’ that appeared in the training data, and examine the effect of applying the intensifier ‘very’ on the sentiment score. [sent-348, score-0.6]

67 As we can see, the matrix-space model learns a matrix that intensifies both ‘bad’ and ‘good’ in the correct sentiment scale, i. [sent-349, score-0.643]

68 , ξ(good) < ξ(very good) and ξ(bad) < ξ(very bad), while the bag-of-words model gets the sentiment of ‘very bad’ wrong: it is more positive than ‘bad’ . [sent-351, score-0.561]

69 The matrix-space model correctly encodes the effect of the negator for both positive and negative adjectives, such that ξ(not good) < ξ(good) and ξ(bad) < ξ(not bad). [sent-353, score-0.214]

70 each word is represented as a function, more specifically a linear operator, and the function composition defined as matrix multiplication, we can think of ”not very” being an operator itself, that is a composition of operator ”not” and operator ”very”. [sent-358, score-0.491]

71 There has been a lot of research in determining the sentiment of words and constructing polarity dictionaries (Hatzivassiloglou and McKeown, 1997; Wiebe, 2000; Rao and Ravichandran, 2009; Mohammad et al. [sent-360, score-0.673]

72 Some recent work is trying to identify the degree of sentiment of adjectives and adverbs from text using co-occurrence statistics. [sent-363, score-0.57]

73 al (201 1) and Liu and Seneff (2009), suggest ways of computing the sentiment of adjectives from data, and computing the effect of combining adjective with adverb as multiplicative effect and combining adjective with negation as additive effect. [sent-365, score-1.025]

74 However these models require the knowledge of a part of speech of given words and the list of negators (since the negator is an adjective as well). [sent-366, score-0.23]

75 On the other hand, there has been some research in trying to model compositional effects for sentiment at the phrase- and sentence-level. [sent-368, score-0.752]

76 Choi and Cardie (2008) hand-code compositional rules in order to model compositional effects ofcombining different words in the phrase. [sent-369, score-0.446]

77 Another recent work that tries to model the compositional semantics of combining different words is Nakagawa et. [sent-371, score-0.232]

78 (2010), which proposes a model that learns the effects of combining different words using phrase/sentence dependency parse trees and an initial polarity dictionary. [sent-373, score-0.241]

79 They present a learning method that employs hidden variables for sentiment classification: given the polarity of a sentence and the a priori polarities of its words, they learn how to model the interactions between words with headmodifier relations in the dependency tree. [sent-374, score-0.673]

80 Our task is different: we classify phrases according to a single ordinal scale that combines both polarity and strength. [sent-379, score-0.402]

81 In the current work we look at finegrained sentiment analysis, more specifically we study word representations for use in true compositional semantic settings. [sent-381, score-0.708]

82 They define composition as an additive or multiplicative function of two vectors and show that compositional approaches generally outperform non-compositional approaches that treat the phrase as the union of single lexical items. [sent-385, score-0.497]

83 It shows that modeling adjectives as linear transformations and applying those linear transformations to nouns results in final vectors for adjective-noun compositions that are close in semantic space to other similar phrases. [sent-387, score-0.176]

84 The authors argue that modeling adjectives as a linear transformation is a better idea than using additive vector-space models. [sent-388, score-0.188]

85 In this work, a separate matrix for each adjective is learned using the Partial Least Squares method in a completely unsupervised way. [sent-389, score-0.222]

86 — 6 Conclusions and Future work In the current work we present a novel matrix-space model for ordinal scale sentiment prediction and an algorithm for learning such a model. [sent-393, score-0.743]

87 The proposed model learns a matrix for each word; the composition of words is modeled as iterated matrix multiplication. [sent-394, score-0.432]

88 The matrix-space framework with iterated matrix multiplication defines an elegant framework for modeling composition; it is also quite general. [sent-395, score-0.241]

89 We use the matrix-space framework in the context of sentiment prediction, a domain where interesting compositional effects can be observed. [sent-396, score-0.752]

90 as a matrix) for use in true compositional semantic settings. [sent-398, score-0.201]

91 One of the benefits of the proposed approach is that by learning matrices for words, the model can handle unseen word compositions (e. [sent-399, score-0.187]

92 Though in our model the order of composition is the same as the word order, we believe that a linguistically informed order of composition can give us further performance gains. [sent-406, score-0.244]

93 One interesting direction to explore might be to use non-negative matrix factorization (Lee and Seung, 2001), co-clustering techniques (Dhillon, 2001) to better initialize words that share similar contexts. [sent-414, score-0.19]

94 The other possible direction is to use existing sentiment lexicons and employ- ing a “curriculum learning” strategy (Bengio et al. [sent-415, score-0.507]

95 Learning with compositional semantics as structural inference for subsentential sentiment analysis. [sent-440, score-0.708]

96 What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis. [sent-445, score-0.573]

97 Seeing stars when there aren’t many stars: Graph-based semi- supervised learning for sentiment categorization. [sent-470, score-0.548]

98 Dependency tree-based sentiment classification using crfs with hidden variables. [sent-518, score-0.507]

99 Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. [sent-522, score-0.546]

100 Assessing sentiment of text by semantic dependency and contextual valence analysis. [sent-553, score-0.546]

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