H is N et: A Polarity Lexicon based on W ord N et for Emotion Analysis (2021)

Abstract: Dictionary-based methods in sentiment analysis have received scholarly attention recently, the most comprehensive examples of which can be found in English. However, many other languages lack polarity dictionaries, or the existing ones are small in size as in the case of SentiTurkNet, the first and only polarity dictionary in Turkish. Thus, this study aims to extend the content of SentiTurkNet by comparing the two available WordNets in Turkish, namely KeNet and TR-wordnet of BalkaNet. To this end, a current Turkish polarity dictionary has been created relying on 76,825 synsets matching KeNet, where each synset has been annotated with three polarity labels, which are positive, negative and neutral. Meanwhile, the comparison of KeNet and TR-wordnet of BalkaNet has revealed their weaknesses such as the repetition of the same senses, lack of necessary merges of the items belonging to the same synset and the presence of redundant narrower versions of synsets, which are discussed in light of their potential to the improvement of the current lexical databases of Turkish.

## introduction a wordnet can be described as a highly comprehensive dictionary which provides semantic relationships such as synonymy, hypernymy, hyponymy, meronymy, homonymy etc. these rich lexical sources are used for many tasks such as word sense disambiguation, text analysis, information retrieval, and sentiment analysis. there are two wordnets for turkish, namely tr-wordnet of balkanet @xcite (hereafter, trwordnet, which means turkish wordnet) and kenet @xcite @xcite @xcite . whereas tr-wordnet has been created earlier with a smaller scope of synsets, kenet has been created later with a much wider range of synsets than that of tr-wordnet (see section 2 for a more detailed comparison). although the newer wordnets such as kenet are more exhaustive than the earlier ones due to their increased number of synsets, it must be noted that it is also possible to come across instances where the less inclusive wordnets, tr-wordnet can actually reveal the shortcomings of the larger ones. therefore, comparisons of the available synsets for a given language are a good way to improve the available sources as they, by complementing one another, give us the chance to combine the powerful aspects of different wordnets and develop a more thorough dataset for performing various tasks such as sentiment analysis. in recent years, sentiment analysis studies have gained significance in nlp applications. currently, popular sentiment analysis applications frequently employ data regarding product interpretation, film interpretation, service evaluation and political events, mostly extracted from social media platforms. the aim of sentiment analysis is to reveal all emotions and commentary present in the data examined. there are several applicable methods for this purpose, one of which is the dictionary-based method where a polarity dictionary is employed. exploiting a dictionary-based method necessitates the construction of a specific polarity dictionary in the same language as the data-to-beanalyzed. the reason behind this necessity stems from the improbability of creating a universal polarity dictionary due to both grammatical and cultural asymmetries between languages. for instance, a certain historical event can have positive connotations in one culture and negative connotations in another culture. thus, it is an essential step to create a language specific polarity dictionary. in our study, we present a polarity dictionary to provide an extensive polarity dictionary for turkish that dictionary-based sentiment analysis studies have been longing for foot_0 . our primary objective is to provide a more refined and extensive polarity dictionary than the previous sentiturknet. in doing so, we have resorted to a different network from the referenced study. we have identified approximately 76,825 synsets from kenet, which then were manually labeled as positive, negative or neutral by three native speakers of turkish. subsequently, a second labeling was further made on positive and negative words as strong or weak based on their degree of positivity or negativity. in this paper, we will first discuss the literature on wordnets and polarity lexicons in section 2, then proceed to present the comparison of kenet and tr-wordnet in section 3. in section 4, we explain how we have constructed our comprehensive polarity lexicon, hisnet. subsequently in section 5, we present the statistical comparison of hisnet to sentiturknet. lastly, we make our concluding remarks in section 6. ## polarity lexicon generation: hisnet this study aims to enlarge sentiturknet in terms of synset number by using a different turkish wordnet. for this study, we used the most comprehensive word network available as the turkish wordnet: kenet @xcite @xcite @xcite . was created with the data obtained from the current items in turkish lexicon, and emerged following the turkish wordnet. compared to turkish wordnet, kenet has a larger synset rate, which is the reason why we opted for kenet over turkish wordnet for the purposes of this study. as the first step of our project, we have identified approximately 76,825 synsets from kenet. subsequently, all of these synsets were manually labeled as positive, negative or neutral by three native speakers of turkish. this recursive labeling process is necessary to train the classifiers where the polarity values will then be determined. the first labelling process resulted in 3,100 positive, 10,191 negative and 63,534 neutral data, during which decisions were based on the meaning and connotation of each word. as the polarity of such connotations are subjective by nature, and thus, we have attended to the majority's label when there is a discrepancy between the annotators. for instance, the word for flower, "c ¸ic ¸ek," may have positive connotations for an individual, yet another individual may find flowers repulsive because of their allergies. after the first round of labeling, the words tagged as "neutral" consisted the majority. following the first labelling, a second labelling process was conducted for the words which were labeled as positive and negative in the first round. to be more specific, the words were re-labeled based on the degree of their positivity or negativity as strong or weak. there was no second labeling on objective words. after the second marking, we found that the weak positive and weak negative tags were more prominent. for instance, the word mükemmel (excellent) in turkish has been marked three times. thus, three different views were obtained for the value of this word. in this example, after it was decided that the value of the word 3,360 mükemmel (excellent) was positive, it was evaluated whether the positive value was weak or strong in the second stage. while selecting the appropriate label, the compatibility of the labels selected by the three labelers was also evaluated. to put it differently, if a positive word receives strong label from all three annotators, it is regarded as strong positive. if it receives two strong and one weak label, it is considered as very positive. if it is labelled as strong once and as weak twice, it means it is just positive. finally, if it receives weak label from all three annotators, it is considered as weak positive. the same is also true for the words labelled as negative. finally, the automatic analysis processes will be easier and more accurate in turkish with the assignment of such polarity values to words. we believe that tagging words from kenet data and comparing them to wordnet in english will lead us to conduct better analyses. moreover, providing the sentiment analysis solutions with marked data will enhance their performance. ## conclusion dictionary-based sentiment analysis studies in languages except english are very limited due to the scarcity of sources regarding polarity. we conclude that translating sources of polarity from english to turkish is not a viable approach to create a turkish polarity dictionary since not all terms in one language have equivalent terms in other languages. furthermore, the same terms may have different degrees of polarity due to the cultural discrepancies. to this end, the most prominent contribution of this study is to present hisnet, a new polarity lexicon for turkish by extending the volume of sentiturknet, the existing first and only polarity dictionary available in turkish. we expect that hisnet can prove itself as a useful tool for sentiment analysis applications in turkish thanks to its exhaustive coverage of the synsets in turkish wordnet. in this paper, we have also presented a comparison of two available wordnets for turkish, which is crucial to do so when there are multiple sources for a given language for further improvements. our comparison has shown that both trwordnet and kenet have their shortcomings. to sum up, such comparisons may present a detailed picture of what steps need to be taken to improve the available wordnets as they provide the available sources for a language in a comparative way.