Creating Domain Dependent T urkish W ord N et and S enti N et (2021)

Abstract: A WordNet is a thesaurus that has a structured list of words organized depending on their meanings. WordNet represents word senses, all meanings a single lemma may have, the relations between these senses, and their definitions. Another study within the domain of Natural Language Processing is sentiment analysis. With sentiment analysis, data sets can be scored according to the emotion they contain. In the sentiment analysis we did with the data we received on the Tourism WordNet, we performed a domain-specific sentiment analysis study by annotating the data. In this paper, we propose a method to facilitate Natural Language Processing tasks such as sentiment analysis performed in specific domains via creating a specific-domain subset of an original Turkish dictionary. As the preliminary study, we have created a WordNet for the tourism domain with 14,000 words and validated it on simple tasks.

## introduction wordnet is a semantic network that represents semantic relations between different concepts by providing a graph consisting of nodes and links. a semantic network is a sine qua non of nlp applications which aim to integrate domain knowledge and lexical knowledge. to this end, since the primary purpose of using wordnet is obtaining the similarities and relations between words, wordnets have been employed in various fields of nlp such as word sense and root word disambiguation, information retrieval, machine translation, and sentiment analysis. sentiment analysis interprets and classifies the emotions in a data through natural language processing learning. it can be performed on a word, a sentence, or even a paragraph. with sentiment analysis, many data such as surveys, texts, customer comments and social media content can be analyzed. especially in the business world, it has a very important place in understanding customers, so that products and services can be arranged to meet the needs. among many fields of nlp emplying word-net, sentiment analysis, or opinion mining, refers to the study of people's opinions, sentiments, appraisals, attitudes, and emotions towards entities, which might include products, services, organizations, issues, individuals or events @xcite . sentiment analysis primarily deals with opinions that express or imply positive, negative or neutral sentiments. in order to conduct such analyses, wordnets are of great importance since they provide data in an organized way, especially when the study relies on domain-specific data as in this study. usually created for general usage, a word-net can also be created and used for specific domains such as tourism, textile or technology, each of which may inherently contain different senses and relations of the same words. that is to say, depending on the wordnet one draws on, the output of sentiment analysis may change. to illustrate, being 'thick' has positive connotations for a carpet whereas it is often undesirable for smart phones. therefore, conducting a sentiment analysis in a specific domain necessitates the creation of a domain-specific wordnet. prior to the creation of a wordnet, a lexicon with broad coverage should be created in the first place. however, there is no limit for the number of words in a lexicon for agglutinative languages like turkish. in addition to agglutination, polysemy, i.e., the coexistence of many possible meanings for a word creates hundreds of basic semantic inconsistencies, which indicates that covering all the words and their senses in a language is a highly demanding task. for instance, a turkish lexicon carries more than 50,000 words; nevertheless, employing such a vast lexicon for a specific domain brings out ambiguous results since it leaves out the words that are not prevalent in daily usage but common in that specific domain. to this end, this study aims to address the issue of utilizing an immense wordnet for a specific domain, namely tourism. the data for the study consists of online user reviews and preferences of a tourism company located in turkey. drawing on this data, we have initially corrected the misspelled words, put them into groups depending on their part of speech (noun, proper noun, adjective, verb and adverb), and finally tagged them based on the linguistic features of turkish. these steps have provided us with a 14,000-word lexicon covering not only commonly used words but also domainspecific words. compared to currently used turkish dictionaries, this newly created dictionary has approximately four times fewer words, which is the reason why we draw on this dictionary while creating the domain-specific wordnet. as expected, the meanings of the words in this domain specific dictionary vary based on their area of usage. that is to say, the meanings of some words in general use acquire new meanings in the domainspecific dictionary, according to which we have arranged the hierarchy of the words. the necessary data for sentinet, which is a domain-dependent resource for sentiment analysis, have been drawn from the tourism wordnet we created. it should be said that the data used for sentiment analysis were matched with their counterpart in turkish wordnet again after annotating. the tourism wordnet and sentinet data are linked to each other via senses. the synset ids of sentinet and tourism wordnet data are the same on both sides. in the annotating phase, care has been taken to annotate all data with more than one annotator and to ensure these annotators do not have information about each other's preferences. although the line of objectivity is not possible for sentiment analysis markings, it is aimed to present a study that yields more successful results with these items that we pay attention to in the mark-ing stages. this paper is organized as follows: we first discuss the relevant literature on wordnets in section 2. we explain how we generated the domain dependent wordnet and sentinet in sections 3 and 4. we provide details on the word-sense disambiguation task using our domain dependent wordnet in section 5. the statistics and experimental results regarding our wordnet and sentinet are given in section 6. lastly, we conclude in section 7. ## literature review the first wordnet project is a lexical database for english, namely princeton wordnet (pwn), which was initiated in 1995 by @xcite . currently, the latest release of pwn, version 3.1, has 117,000 synsets, and 206,941 wordsense pairs. a more detailed history and description of pwn is given in @xcite . shortly after the release of pwn, wordnets for other languages have been constructed although their coverage is not as extensive as that of pwn, @xcite , @xcite . for balkan languages, balkanet @xcite is the most comprehensive work up to date. for the turkish wordnet part of balkanet @xcite , the researchers automatically extracted the synonyms, antonyms and hyponyms from a monolingual turkish dictionary. the most comprehensive turkish wordnet is kenet, which has 80,000 synsets covering 110,000 word-sense pairs @xcite @xcite @xcite . all this body of work mentioned above has been created and used for general purposes. however, the creation of a domain-specific wordnet is a more recent phenomenon, of which there are relatively few examples. archiwordnet is a wordnet created specifically for the architecture and construction domain drawing on italian/english bilingual resources. similarly, jur-wordnet is another example of a domain-specific wordnet which was created as an extension for the legal domain of ital-wordnet by providing multilingual access to legal information sources. specifically created to be used for software engineering tasks, sethesaurus is a dictionary constructed based on informal discussions about programming on social platforms. by generating a wordnet specific to the tourism domain, we hope to contribute to this body of work, and provide inspiring ideas for future studies @xcite @xcite . regarding sentiment analysis, to the best of our knowledge, there have been no studies conducting a domain-specific sentiment analysis relying on a domain-specific wordnet. therefore, it would be plausible to assert that we are presenting a pioneering study in this field. ## doman dependent sentinet since sentiment analysis focuses on whether entities are positive, negative or neutral, the words in our tourism corpus have been labeled as positive, negative or neutral by three annotators, who are native speakers of turkish. following the first labeling process, the words labeled as positive and negative have been subjected to a second labeling process, marked as strong or weak since the degree of positivity or negativity may vary as in the difference between the words "güzel" (beautiful) and "harika" (excellent). this allows a more precise analysis of the positive or negative value that the word adds to the sentence. furthermore, we have paid attention to both the dictionary meaning of the word and the way it is used in daily life in this specific domain. in cases where a word was labeled differently by the annotators, we have relied on the opinion of the majority. following the labeling process, we have found that the majority of the words are neutral while the ratio of negative words is higher than positive words. moreover, we have found that the weak positive and weak negative tags are more prevalent than the strong positive and strong negative. in addition, the automated analysis of the sentences are accelerated since the positive, negative and neutral values of the words can be better processed by the algorithm. therefore, we believe that the automatic analysis of the words will be much easier and faster. ## usage of wordnet in semantic annotation: all-words sense annotation the study has been conducted on a 20,000sentence corpus created using data from the tourism domain. the words and their definitions have been drawn from the tourism wordnet. two interfaces have been created to employ in the se-mantic annotation process, which consisted of two steps. the sentences were processed by the annotators after each word was subjected to morphological analysis and matched with its equivalent in the turkish wordnet. four annotators worked simultaneously in the first step using the interface that displays each sentence individually. as can be seen in figure 1 , each word can be annotated individually, and the buttons at the top are used to navigate the corpus. when a word is clicked on, a list of every possible definition is displayed. the annotators chose the appropriate definition manually. punctuation marks were annotated automatically. the annotators also made use of the "annotate each occurrence of the word with the same definition" feature, making the process semiautomatic and increasing efficiency. this feature annotates all occurrences of the selected word in the corpus with the same definition from the list. through this feature, words that happen to only have a single definition, in general or in this specific domain, have been annotated more easily. sentences that produced errors in the morphological analysis phase were corrected manually using the same analyzer. each word was annotated primarily using the definitions in the tourism wordnet. the definitions in the turkish wordnet were made use of where the tourism wordnet was not sufficient. at the end of the first step, there were still words without annotations. the second step was an effort to fill in these gaps and check the results manually. a different interface displaying all sentences simultaneously was used in this step. the words were arranged alphabetically, and grouped based on their sentences. in this way, the words were compared to one another in different contexts, and their definitions were decided on by reviewing the entire corpus. the missing annotations were completed based on the existing ones. two annotators worked on this step in cooperation in order to ensure consistency between their annotations. in the annotation process, an optional automatic annotation function was also employed. this function automatically matches the words with only one definition in the dictionary with that one definition without asking the annotator. afterwards, these were verified by the annotators and corrected when necessary. the semantic annotation interface can also detect multi-word expressions, which allows the annotation of words that come together to form a single unit of meaning. turkish has a great volume of two-word verbal expressions (e.g. "kabul etmek", to accept; "memnun kalmak", to be satisfied"), which is reflected in the tourism corpus. the senses that do not show up when these words occur by themselves are included in the list of possible definitions if they appear consecutively in the right order, which the annotators chose manually. ## conclusion overall, we have created a domain-specific lexicon with user reviews and preferences from the tourism domain. based on this newly created lexicon, we have designed a novel wordnet, and employed it for domain-specific sentiment analysis. by doing so, we have managed to mitigate the disambiguation problem for this specific domain. finally, we have improved the performance of sentence-based morphological analysis by approximately 7% in the tourism domain.