The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.
Currently, methods devised to perform sentiment analysis and, more specifically, polarity classification range from machine learning to lexical-based approaches. While machine learning methods have proved useful in scenarios where a large amount of training data is available along with top quality NLP resources (such as taggers, parsers and others), they usually have low performance in opposite scenarios. Since most non-English languages face resource limitations, for example Portuguese, lexical-based approaches have become very popular. Some works following this line are @cite_24 @cite_30 @cite_1 .
@cite_24 @cite_1 @cite_30
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Opinion Lexicons are linguistic resources annotated with semantic orientation of terms (positive and negative) and are important for opinion min- ing tasks. In the literature we see a variety of proposals for the construction of opinion lexicons using different linguistic resources and techniques. In this work, we propose and evaluate the integration of such linguistic resources to create a single lexicon for the Portuguese language. This paper presents some results on lexicon-based classification of sentiment polarity in web reviews of products written in Brazilian Portuguese. They represent a first step towards a robust opinion miner from reviews of technology products. The evaluation shows the performance of 3 different sentiment lexicons combined with simple strategies. It is also discussed the risk of considering the rating provided by the writers for the purpose of evaluating the algorithms. The results show that the better combination is the version of the algorithm that deals also with negation and intensification and uses the sentiment lexicon Sent ilex. The average F-measure achieved 0.73. This work presents an evaluation of the Brazilian Portuguese LIWC dictionary for Sentiment Analysis. This evaluation is conducted by comparison against two other sentiment resources for Portuguese language: Opinion Lexicon and SentiLex. We conducted an intrinsic and an extrinsic evaluations and show how LIWC dictionary could be used in sentiment analysis projects.