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This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github 1

With a rapid increase in e-commerce websites, people are often interested in analyzing customer reviews expressing customer sentiments on different features of a product before making purchase decisions. In this paper, we present ABSA (Aspect-Based Sentiment Analysis) Toolkit developed for performing aspect-level sentiment analysis on customer reviews. The system has two main phases: (a) development phase and (b) production phase. The development phase allows a user to train models for performing aspect level sentiment analysis tasks on the target domain. In the production phase, a web application is provided through which an end user can submit reviews to analyze aspect level sentiments. The system is built using state-of-the-art approaches of aspect term extraction, aspect category detection, and aspect polarity identification. To the best of our knowledge, there is no framework publicly available to build aspect-level sentiment analysis application. All the source code of the ABSA toolkit is available on GitHub.

This paper presents a sentiment analysis approach based on Markov chains for predicting the sentiment of Urdu tweets. Sentiment analysis has been a focus of natural language processing (NLP) research community from the past few decades. The reason for this growing interest is twofold. First, the complexity involved in identifying sentiment from the unstructured text makes it a challenging problem for the research community. Second, sentiment analysis has a wide variety of applications ranging from industry to academia has made it a popular area in the research field of NLP. However, very little work has been done on sentiment analysis for the low resource languages which include Urdu, Bengali, Hindi, and other Asian languages. This work focuses on developing a 3-class (positive, negative, and neutral) sentiment classification model for the Urdu language. The experiments were conducted on the labeled corpus of Urdu tweets extracted from the Twitter network. One of the main contributions of this research includes the development of a large labeled corpus of Urdu Tweets for sentiment analysis. To the best of our knowledge, there is no such corpus available publicly in the Urdu Language. The labeled dataset is available on GitHub (https ://githu b.com/zarme en92/urdut weets). Furthermore, the results showed that the proposed approach outperforms the lexicon-based and traditional machine learning-based approaches of sentiment analysis.

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