IIITG - ADBU at S em E val-2020 Task 9: SVM for Sentiment Analysis of E nglish- H indi Code-Mixed Text (2020)

Abstract: In this paper, we present the results that the team IIITG-ADBU (codalab username ‘abaruah’) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.

## introduction sentiment analysis has been defined as the computational study of opinions, sentiments, and emotions expressed in the text @xcite . in its basic form, sentiment analysis is used to determine the polarity of a given text where the polarity may be negative, neutral, and positive. thus, sentiment analysis can be viewed as a text classification problem. with the advent of social media platforms, the use of non-standard language has increased. nowa-days, it is very common to use emoticons, mentions, acronyms, and ungrammatical sentences while communicating in social media. all these factors make the traditional tools used for natural language processing fail on social media text. another new style of communication in social media is the use of code-mixed text. code-mixing means the mixing of words from more than one language in the same sentence or between sentences. code mixing makes the task of sentiment analysis more challenging. the objective of sentimix (task 9), organized as part of the international workshop on semantic evaluation 2020 (semeval 2020), is to detect the sentiment of code-mixed tweets @xcite . this task was a three-way classification problem with the labels being negative, neutral, and positive. the task was held for both hindi-english and spanish-english code-mixed tweets. we participated in this task for the hindi-english language. in this task, we experimented with svm and multilingual bert classifiers. ## data set the hindi-english data set for this task was provided in the conll format. for each tweet, the following information was provided: (1) the id for the tweet, (2) its label (negative, neutral, or positive), and (3) the language id of each token (hin for hindi, eng for english, and o if neither hindi nor english). in our experiments, we did not make use of the language id information provided in the data set. tables 1 to 4 show the statistics of the trial, train, development, and the combined data sets respectively. the combined data set was obtained by combining the trial, train, and development data sets. the duplicate entries were removed from the combined data set. the combined data set was used in our experiments. as can be seen from the tables, the data sets were quite balanced. ## results