Abstract - In this paper we propose to approach the subject of detecting relevant tweets when in the presence of very large tweet collections containing a large number of different trending topics. We use a large database of tweets collected during the 2011 London Riots as a case study to demonstrate the application of the proposed techniques. In order to extract relevant content, we extend, formalize and apply a recent technique, called Twitter Topic Fuzzy Fingerprints, which, in the scope of social media, outperforms other well known text based classification methods, while being less computationally demanding, an essential feature when processing large volumes of streaming data. Using this technique we were able to detect 45\% additional relevant tweets within the database.
Abstract - This paper examines the suitability of applying fuzzy semantic similarity measures (FSSM) to the task of detecting potential future events through the use of a group of prototypical event tweets. FSSM are ideal measures to be used to analyse the semantic textual content of tweets due to the ability to deal equally with not only nouns, verbs, adjectives and adverbs, but also perception based fuzzy words. The proposed methodology first creates a set of prototypical event related tweets and a control group of tweets from a data source, then calculates the semantic similarity against an event dataset compiled from tweets issued during the 2011 London riots. The dataset of tweets contained a proportion of tweets that the Guardian Newspaper publically released that were attributed to 200 influential Twitter users during the actual riot. The effects of changing the semantic similarity threshold are investigated in order to evaluate if Twitter tweets can be used in conjunction with fuzzy short text similarity measures and prototypical event related tweets to determine if an event is more likely to occur. By looking at the increase in frequency of tweets in the dataset, over a certain similarity threshold when matched with prototypical event tweets about riots, the results have shown that a potential future event can be detected.
Abstract - Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naive Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.
Abstract - In the Sentiment Analysis area, several Sentiment Analysis Methods are able to extract different polarities from written text with different rates. As it is known, the vote of majority has a high impact in decision making. We propose the use of Induced Ordered Weighted Averaging operators based on fuzzy majority for aggregating polarities from several Sentiment Analysis Methods. The main contribution of this work is to set neutrality on opinions guided by fuzzy majority. We aim to improve classification results by removing those neutral reviews labelled by a consensus of a collection.
Abstract - Over the past several years, social networking services or micro-blogs have become ubiquitously accessible anytime and contain users' opinions expressed in the form of short text messages. In this paper, we introduce a new automatic approach named FEmoRec for emotional context recognition from online social networks that applies a semantic similarity measure based on Multi-Layer Perceptron Neural Net Model. We rely on the assumption that a tweet may belong to many emotional categories with different membership degrees. We classify the tweet by computing an emotion vector that represents the tweet's fuzzy membership values to Ekman's emotion classes. Carried out experiments emphasize the relevance of our proposal, compared to other methods.