IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 827–831, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News Zarmeen Nasim Institute of Business Administration (IBA) Karachi, Pakistan znasim@khi.iba.edu.pk Abstract This paper presents the details of our sys- tem IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our sys- tem participated in both tracks. For mi- croblogs track, a supervised learning ap- proach was adopted and the regressor was trained using XgBoost regression algo- rithm on lexicon features. For news head- lines track, an ensemble of regressors was used to predict sentiment score. One re- gressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github 1 1 Introduction Sentiment Analysis has become a very active area of research during the last decade. The reason be- hind this rising popularity is twofold. First, senti- ment analysis has a great number of applications varying from academia to commercial domains such as customer support, brand management, so- cial media marketing e.t.c. Second, sentiment analysis involves a number of challenges such as handling unstructured and noisy text, anaphora resolution, context understanding and many oth- ers. Sentiment Analysis now becomes an interest- ing area of research in the Financial domain also. Researchers have shown that the consumer opinions and sentiments have a profound impact on market dynamics [(Goonatilake and Herath, 2007),(Van de Kauter et al., 2015)]. This fur- ther leads to the research interest in predicting stock market from social media discussions and 1https://github.com/zarmeen92/ IBA-Sys-SemEval-2017 news text (Bollen et al., 2011). Earlier attempts of sentiment analysis in Financial domain includes the work of McDonald and Loughran (Loughran and McDonald, 2011) in 2011. They developed the list of words with associated sentiment po- larities for classifying sentiment in financial text. The SemEval 2017 Fine-grained sentiment analy- sis on financial microblogs and news task (Cortis et al., 2017) aims at identifying bullish(optimistic) and bearish(pessimistic) sentiment associated with companies and stocks. This task involved two tracks. Track 1 included microblog messages and track 2 included the dataset of news statements and headlines. In both tracks, the task was to predict the sentiment score for a stock (in track 1) and for a company (in track 2) in a given instance of text. The challenging part was that the sentiment values are on a continuous scale between -1(very nega- tive) to +1(very positive) rather than discrete la- bels. IBA-Sys participated in both subtasks. For sub- task 1, our system was trained to predict the senti- ment score on the given microblog with relevance to the given cashtag. For subtask 2, our system was trained to predict the sentiment score on the given piece of headline with relevance to the given company name. Our system IBA-Sys participated in both tracks. In track 1, we were among top 5 teams whereas, in track 2, our system …

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