Text pre-processing of multilingual for sentiment analysis based on social network data

Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition

Automatic Analysis of Facebook Posts and Comments Written in Brazilian Portuguese

Social networks and media are becoming increasingly important sources for knowing people\u27s opinions and sentiments on a wide variety of topics. The huge number of messages published daily in these media makes it impractical to analyze them without the help of natural language processing systems.This article presents an approach to cluster texts by similarity and identifying the sentiments expressed by comments on then (positive, negative and neutral, among others) in an integrated manner. Unlike most of the available studies that focus on the English language and use Twitter as a data source, we treat Brazilian Portuguese posts and comments published on Facebook. The proposed approach employs an unsupervised learning algorithm to group posts and a supervised algorithm to identify the sentiments expressed in comments to posts. In an experimental evaluation, a system that implements the proposed approach showed similar accuracy to that of human evaluators in the tasks of clustering and sentiment analysis, but performed the tasks in much less time

Aplicaciones de la estadística al framing y la minería de texto en estudios de comunicación

Techniques of discourse analysis in the media have undergone a great evolution thanks to the ‘framing’ theory, but over time it has been perceived that the researchers’ studies could include high levels of subjectivity. To solve this problem, more objective methodologies have been developed, adapting statistical techniques to the examination of the main frames; cluster analysis being one of the most significant elements within these processes. More recent methods, such as text mining, propose the analysis to be entirely done through computer algorithms with morphological knowledge of different languages. This article approaches the state of the art of these methodologies, their most outstanding tools and the computer softwares that help in the statistical analysis of framing, with the aim of systematizing the options that are currently available for communication research

Near Real-Time Sentiment and Topic Analysis of Sport Events

Sport events’ media consumption patterns have started transitioning to a multi-screen paradigm, where, through multitasking, viewers are able to search for additional information about the event they are watching live, as well as contribute with their perspective of the event to other viewers. The audiovisual and multimedia industries, however, are failing to capitalize on this by not providing the sports’ teams and those in charge of the audiovisual production with insights on the final consumers perspective of sport events. As a result of this opportunity, this document focuses on presenting the development of a near real-time sentiment analysis tool and a near real-time topic analysis tool for the analysis of sports events’ related social media content that was published during the transmission of the respective events, thus enabling, in near real-time, the understanding of the sentiment of the viewers and the topics being discussed through each event.Os padrões de consumo de media, têm vindo a mudar para um paradigma de ecrãs múltiplos, onde, através de multitasking, os telespetadores podem pesquisar informações adicionais sobre o evento que estão a assistir, bem como partilhar a sua perspetiva do evento. As indústrias do setor audiovisual e multimédia, no entanto, não estão a aproveitar esta oportunidade, falhando em fornecer às equipas desportivas e aos responsáveis pela produção audiovisual uma visão sobre a perspetiva dos consumidores finais dos eventos desportivos. Como resultado desta oportunidade, este documento foca-se em apresentar o desenvolvimento de uma ferramenta de análise de sentimento e uma ferramenta de análise de tópicos para a análise, em perto de tempo real, de conteúdo das redes sociais relacionado com eventos esportivos e publicado durante a transmissão dos respetivos eventos, permitindo assim, em perto de tempo real, perceber o sentimento dos espectadores e os tópicos mais falados durante cada evento