IITPSemEval: Sentiment Discovery from 140 Characters

Handbook of Research on Soft Computing and Nature-Inspired Algorithms, 2017

Online social networking platforms, such as Weblogs, micro blogs, and social networks are intensively being utilized daily to express individual's thinking. This permits scientists to collect huge amounts of data and extract significant knowledge regarding the sentiments of a large number of people at a scale that was essentially impractical a couple of years back. Therefore, these days, sentiment analysis has the potential to learn sentiments towards persons, object and occasions. Twitter has increasingly become a significant social networking platform where people post messages of up to 140 characters known as ‘Tweets'. Tweets have become the preferred medium for the marketing sector as users can instantly indicate customer success or indicate public relations disaster far more quickly than a web page or traditional media does. In this paper, we have analyzed twitter data and have predicted positive and negative tweets with high accuracy rate using support vector machine (...

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014

We describe the submission of the team of the Sofia University to SemEval-2014 Task 9 on Sentiment Analysis in Twitter. We participated in subtask B, where the participating systems had to predict whether a Twitter message expresses positive, negative, or neutral sentiment. We trained an SVM classifier with a linear kernel using a variety of features. We used publicly available resources only, and thus our results should be easily replicable. Overall, our system is ranked 20th out of 50 submissions (by 44 teams) based on the average of the three 2014 evaluation data scores, with an F1-score of 63.62 on general tweets, 48.37 on sarcastic tweets, and 68.24 on LiveJournal messages.

We present two systems developed at the University of Ottawa for the SemEval 2013 Task 2. The first system (for Task A) classifies the polarity / sentiment orientation of one target word in a Twitter message. The second system (for Task B) classifies the polarity of whole Twitter messages. Our two systems are very simple, based on supervised classifiers with bag-ofwords feature representation, enriched with information from several sources. We present a few additional results, besides results of the submitted runs.

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014

In this paper we report our works for SemEval-2014 Sentiment Analysis in Twitter evaluation challenge. This is the first time we attempt for this task, and our submissions are based on supervised machine learning algorithm. We use Support Vector Machine for both the tasks, viz. contextual polarity disambiguation and message polarity classification. We identify and implement a small set of features for each the tasks, and did not make use of any external resources and/or tools. The systems are tuned on the development sets and finally blind evaluation is performed on the respective test set, which consists of the datasets of five different domains. Our submission for the first task shows the F-score values of 76.3%,

This paper describes our participation at SemEval-2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to compare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a generic sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets. This work is licensed under a Creative Commons Attribution 4.0 International Licence.

In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a competition, our submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. We also generated two large word-sentiment association lexicons, one from tweets with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated using freely available resources. 1

Community's view and feedback have always proved to be the most essential and valuable resource for companies and organizations. With social media being the emerging trend among everyone, it paves way for unprecedented analysis and evaluation of various aspects for which organizations had to rely on unconventional, time consuming and error prone methods earlier. This technique of analysis directly falls under the domain of "sentiment analysis". Sentiment analysis encompasses the vast field of effective classification of user generated text under defined polarities. There are several tools and algorithms available to perform sentiment detection and analysis including supervised machine learning algorithms that perform classification on the target corpus, after getting trained with training data. Lexical techniques which performs classification on the basis of dictionary based annotated corpus and Hybrid tools which are combination of machine learning and lexicon based algorithms. In this paper we have used Support Vector Machine (SVM) for sentiment analysis in Weka. SVM is one of the widely used supervised machine learning algorithms for textual polarity detection. To analyze the performance of SVM, two pre classified datasets of tweets are used and for comparative analysis, three measures are used: Precision, Recall and F-Measure. Results are shown in the form of tables and graphs.

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015

We present our supervised sentiment classification system which competed in SemEval-2015 Task 10B: Sentiment Classification in Twitter-Message Polarity Classification. Our system employs a Support Vector Machine classifier trained using a number of features including n-grams, dependency parses, synset expansions, word prior polarities, and embedding clusters. Using weighted Support Vector Machines, to address the issue of class imbalance, our system obtains positive class F-scores of 0.701 and 0.656, and negative class F-scores of 0.515 and 0.478 over the training and test sets, respectively.

In this paper, we describe our system for the SemEval-2013 Task 2, Sentiment Analysis in Twitter. We formed features that take into account the context of the expression and take a supervised approach towards subjectivity and polarity classification. Experiments were performed on the features to find out whether they were more suited for subjectivity or polarity Classification. We tested our model for sentiment polarity classification on Twitter as well as SMS chat expressions, analyzed their F-measure scores and drew some interesting conclusions from them.

We propose a method for using discourse relations for polarity detection of tweets. We have focused on unstructured and noisy text like tweets on which linguistic tools like parsers and POS-taggers don't work properly. We have showed how conjunctions, connectives, modals and conditionals affect the sentiments in tweets. We have also handled the commonly used abbreviations, slangs and collocations which are usually used in short text messages like tweets. This work focuses on a Web based application which produces results in real time. This approach is an extension of the previous work (Mukherjee et al. 2012).