OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 670–674, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields Chukwuyem J. Onyibe and Nizar Habash Computational Approaches to Modeling Language (CAMeL) Lab Computer Science Program New York University Abu Dhabi, Abu Dhabi, UAE {chukwuyem.onyibe, nizar.habash}@nyu.edu Abstract We describe a supervised system that uses optimized Conditional Random Fields and lexical features to predict the sentiment of a tweet. The system was submitted to the English version of all subtasks in SemEval-2017 Task 4. 1 Introduction Sentiment analysis, sometimes known as opinion mining, is the process of detecting the contex- tual polarity of text. That is, given a text (of any length), subjective information pertaining to the sentiment attached to the text is derived using nat- ural language processing tools (Pang et al., 2008; Cambria et al., 2013). Sentiment analysis could be approached in two ways. General sentiment anal- ysis, often termed sentence-level sentiment analy- sis, extracts the general sentiment of the text based solely on its contents. The sentiment is not related or based on any external entity. On the other hand, topic-level sentiment analysis infers the sentiment of the given text based on a specific topic. This branch of sentiment analysis has been further ex- plored under the term Stance Detection (Faulkner, 2014; Anand et al., 2011). With the rapid in- crease in different forms of online expression like reviews, political criticism, ratings and punditry, social media has become an invaluable source of data for research in sentiment analysis. With data from social media, sentiment analysis can show the public sentiment towards current topics of pub- lic discourse. Twitter is one of the largest of such social media platforms and a prominent source of data for sentiment research (Pak and Paroubek, 2010; Wang et al., 2011; Rajadesingan and Liu, 2014). In this paper, we describe the components and results of a system for English sentiment anal- ysis with which participated in an international shared task on sentiment analysis for Twitter data. 2 Shared Task Description The SemEval-2017 Task 4 (Rosenthal et al., 2017) (henceforth SemEval) is aimed at categorizing tweets from Twitter. This task is composed of five subtasks. Subtask A is a message polarity classifi- cation task where tweets are classified on general sentiment (not directed at any topic) on a three- way scale: Negative, Neutral and Positive (hence- forth, −1, 0, +1). Subtask B is a topic-based mes- sage polarity classification where tweets are clas- sified on sentiment towards a given topic on a two- way scale: Negative and Positive (henceforth, −1, +1). Subtask C is the same topic-based task as B, except that it uses a five-point sentiment scale (−2, −1, 0, +1, +2), where −2 is very negative and +2 is very positive. Both subtasks D and E are tweet quantification tasks based on subtasks B and C, respectively. In both D and E, given the same datasets from …

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