High, Medium or Low? Detecting Intensity Variation Among polar synonyms in W ord N et (2016)

Abstract: For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives (synonyms) that share the same sense. Choice of a word from a set of synonyms, provides a way to select the exact polarity-intensity. For example, choosing to describe a person as benevolent rather than kind1 changes the intensity of the expression. In this paper, we present a sense based lexical resource, where synonyms are assigned intensity levels, viz., high, medium and low. We show that the measure P (s|w) (probability of a sense s given the word w) can derive the intensity of a word within the sense. We observe a statistically significant positive correlation between P(s|w) and intensity of synonyms for three languages, viz., English, Marathi and Hindi. The average correlation scores are 0.47 for English, 0.56 for Marathi and 0.58 for Hindi.

## introduction sentiment analysis is a crucial task for various web and media outlets, such as, e-commerce websites, blogs and newspapers. the general approach of sentiment analysis is to summarize the semantic polarity (i.e., positive or negative) of sentences/documents @xcite @xcite @xcite . however, sentence intensity becomes crucial when we need to compare sentences having the same polarity orientation. in such scenarios, we can use intensity of words to judge the intensity of a sentence. words that bear the same sense can be used interchangeably to upgrade or downgrade the intensity of the expression. the following example helps illustrate the problem we attempt to address. in addition to english, there exists polarityintensity variation across synonyms in other languages also. consider the following example from hindi: there are several manually or automatically created sense based lexical resources @xcite that assign the same positive or negative polarity to all synonymous words, making no distinction among them in terms of their intensity. in this paper, we address the concept of polarity-intensity variation among synonyms and come up with a measure to predict the polarity intensity of a word for the given sense. we show that there is a statistically significant positive correlation between p (s|w) (probability of sense s given word w) and intensity of a word w within the sense s (section 3). hence, the measure p (s|w) can be used to predict the intensity of a word within the sense. we extensively validate this positive correlation in three languages 3 , viz., english, marathi and hindi (section 5). we observe a statistically significant positive correlation of 0.47 for english, 0.56 for marathi and 0.58 for hindi (section 7). our contribution: our work contributes an automatically generated sense based lexical resource where words which belong to the same sense are assigned three intensity levels, viz., high, medium and low. this resource can be used to derive intensity information of a subjective sentence or document, which essentially empowers existing sentiment analysis systems. in addition to this, intensity information of words can be used to reduce or enhance an over-expressed or under-expressed text respectively. ## related work and discussion several researchers have made successful attempts for finding opinion words @xcite @xcite @xcite @xcite @xcite ; however, finding intensity of words still considered as a challenging task. there have been some works on scaling adjectives by their strength, independent of the sense they express. the first work in the direction of adjectival scale was done by @xcite . they exploited linguistic knowledge available in the corpora to compute similarity between adjectives. however, their approach did not consider polarity orientation of adjectives, they provided ordering among non-polar adjectives like, cold, lukewarm, warm, hot. @xcite demonstrated that vector off-set can be used to derive scalar relationship amongst adjectives. de melo and bansal (2013) used a pat-3 a person who is a linguist as well as a native speaker of the language can annotate words with more accuracy. the availability of the linguists, who are also native speakers of hindi and marathi made us to choose these two languages other than english. hindi and marathi are two of the 23 official languages of india, which have approximately 258 and 73 million speakers respectively. english is chosen, because most of the lexical resources which we had pointed out in our work are in english only. tern based approach to identify intensity relation among adjectives, but their approach had a severe coverage problem. @xcite provided ordering among polar adjectives that bear the same semantic property. none of the existing works address intensity variation among synonyms. however, choice of a word from a set of synonyms provides a way to intensify the expression. our approach pin-points the polarity-intensity variation across synonyms. ## polarity-intensity variation and synonymous words the classical semantic bleaching theory foot_2 states that a word which has high frequency of use tends to have low intensity in comparison to a word having less frequency of use. for example, the frequent use of the word good makes it less intense, while rare use of the word great makes it more intense @xcite . however, good and great are not synonyms according to sentiwordnet. the semantic bleaching phenomenon throws light on the positive association between frequency and intensity regardless of any semantic relation (for example, synonymy). but, when we computed correlation between totalfrequency (section 5) and polarity-intensity within a sense (section 6), we observed a negative correlation. in this paper, we define polarity-intensity variation among synonyms (words having the same sense), specifically for the polar words. words having the same sense cannot be directly compared on the basis of their frequency count in the sense, because their total frequency of usage (total count of the words in all its senses) are different. we need a relative count of the synonymous words, that is, (count of the word with the sense / total count of the word). the cause of overuse of a word is its use in multiple senses @xcite . therefore, use of a word in multiple senses increases the total frequency of use, but the word loses its frequency count with a particular sense relative to the total frequency count of the word. considering this frequency distribution as a base, we hypothesize polarity-intensity variation among words belonging to a particular sense. consider the following derivation that validates our proposed hypothesis for polarity-intensity variation across synonyms. according to the semantic bleaching phenomenon: t c(w 1 ) < t c(w 2 ) ⇒ i(w 1 ) > i(w 2 ) (1) where, t c is a function that gives total-count of a word and i is a function that gives intensity of a word. since w 2 has higher total-frequency (overused) than w 1 , we can deduce that w 2 has more senses in comparison with w 1 . let us assume that w 1 and w 2 are synonyms for i th sense and w 1 has only one sense, that is, i th sense and w 2 has n (@xmath0. now, we rewrite equation 1 in terms of count of words in their senses in equation 2. here, sc k w j represents sense-count, that is, count of the word 'w j ' with the sense 'k'. now, to compare the synonymous words w 1 and w 2 in the i th sense, we need their relative counts in the sense (equation 3 ). relative count is the count of the word with the sense divided by total-count of the word. since w 1 has only one sense, so its sense-count and total-count will remain the same. hence, the fraction we observe a reversal of the @xmath1, but the intensity relation remains intact. essentially, a word that shows its majority occurrence with the sense or has a higher relative frequency count, is more intense for the sense than the other synonymous words. a few such instances of polarity-intensity variation in a sense are shown in ## probability of sense given word statistically, a relative frequency count of a word is nothing but the probability of sense given word (p (s|w)). the function c(w i , s j ) gives count of w i with the sense s j , while the function c(w i ) gives total-count (aggregation of count in all senses). the measure p (s j |w i ) is defined as follows: hence, we deduce that if a word possess higher value for the measure p (s|w), then it is more intense than other synonymous words. equation 5 generalizes the proposed hypothesis. where, w 1 and w 2 belong to the same sense s. in summary, when we compare words within a sense, we need to account for the participation of these words in other senses also. the proposed probabilistic measure, probability of sense given word considers the participation of a word in other senses also in the form of its total-count. we observe a statistically significant positive correlation between polarity-intensity levels assigned by linguists and the value of p (s|w) (relative frequency of a word w in a sense s) (section 7). a high value of p (s|w) is possible in the following scenarios. ## dataset we validate our hypothesis using three languages, viz., english, hindi, and marathi. english: for english, we extracted all the adjective synsets whose polarity (positive or negative) value is greater than 0.5 as per sentiwordnet, except the synsets that have only one word. we ignored the synsets having polarity values less than or equal to 0.5, considering them a weak candidate for polarity-intensity variation phenomenon. with the threshold value of 0.5, we extracted a total of 1116 synsets. however, sentiwordnet is an automatically compiled lexical resource, which assigns polarity values based on corpus dependent probabilistic measures. to make our english dataset potentially conclusive, we asked two linguists in english to manually inspect the polarity orientation of synsets (senses). c(w i ,s j ): for english words, the value of the function c is obtained from the english word-net database file, that is, 'cntlist' foot_4 . for hindi and marathi, we used a sense marked corpus in tourism and health domain 7 (khapra et al., 2010). the total number of sense marked words in each domain are depicted in ## gold standard data preparation we asked two linguists in each language to assign words to different intensity levels, viz., high (3), medium (2), and low (1) within a synset. a discrete scale with only three intensity levels is chosen to reduce the subjectivity issue in annotation, consequently complexity of annotation. consider the following example of synonymous words, where intensity levels are assigned by english linguists. ## empirical validation we validate the hypothesized relation between polarity-intensity and probabilistic measure: p (s|w) by finding pearson product-moment correlation. to test the significance of correlation value, we perform a directional test, that is, t-test using cor.test function of r foot_6 . we obtain a statistically significant positive correlation between gold standard intensity levels and p (s|w) for all the three languages. ## error analysis the observed scenarios that affect the proposed hypothesis negatively are as follows. lang. 2 there are words which are not found in the corpus with any sense of them. in this case, besides the frequency count of the word with the sense, we fail to get evidence for totalcount of the word. in such cases, the probabilistic measure, that is, p (s|w) fails to result in a strong value that can insinuate the correct polarity-intensity of a word, which leads to fall in the correlation estimate. ## conclusion in this paper, we addressed the concept of polarityintensity variation among synonyms. we show that the relative frequency of a word w in a sense s, that is, p (s|w) is a predictor of polarity-intensity of the word in the sense. we present a sense based lexical resource in three languages, where polar synonyms are annotated with the intensity levels, viz., high, medium and low. manual checking of sentiment wordnets for intensity variation is a difficult endeavor. therefore, a by-product of our polarity-intensity analysis is that sentiment wordnets can become more informative resource for sentiment analysis. in addition to this, intensity information of words can be used to reduce or enhance an over-expressed or underexpressed text respectively.