A survey of sentiment analysis in the Portuguese language
TL;DR: This paper categorizes and describes state of the art works involving approaches to each of the tasks of sentiment analysis, as well as supporting language resources such as natural language processing tools, lexicons, corpora, ontologies, and datasets.
Abstract: Sentiment analysis is an area of study that aims to develop computational methods and tools to extract and classify the opinions and emotions expressed by people on social networks, blogs, forums, online shoppings, and others. A lot of research has been developed addressing opinions expressed in the English language. However, studies involving the Portuguese language still need to be advanced to make better use of the specificities of the language. This paper aims to survey the efforts made specifically to address sentiment analysis in the Portuguese language. It categorizes and describes state of the art works involving approaches to each of the tasks of sentiment analysis, as well as supporting language resources such as natural language processing tools, lexicons, corpora, ontologies, and datasets.
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TL;DR: In this paper, the authors proposed a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic.
Abstract: Background: The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet.
Objective: The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil.
Methods: This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts.
Results: In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic.
Conclusions: This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.
42 citations
TL;DR: Results showed that the most appropriate ANN to perform SA in Portuguese is the Convolutional Neural Network, and investment strategies based on Sentiment Analysis can bring profitability both in short and in long term, surpassing the strategies Random Walk and Buy & Hold.
Abstract: The Efficient Market Hypothesis states that stock market changes reflect the arrival of new information through external events and news. Thus, many recent studies in the literature evaluate the impact of Sentiment Analysis (SA) applied to social media and news in the stock market. However, these studies generally do not present investment strategies that take advantage of sentiments in new publications considering the correlation between news and the stock market, specially when news are written in Portuguese. This paper proposes investment strategies based on Sentiment Analysis of financial news applied to the Brazilian stock market. For such, the following activities were performed: (i) identifying the most suitable Artificial Neural Network (ANN) architecture to perform Sentiment Analysis in financial news in Brazilian Portuguese; (ii) studying the correlation between the predominant sentiment in financial news of three major Brazilian news portals through the Granger causality test; (iii) proposing two categories of investment strategies based on Sentiment Analysis, considering both negative and positive financial news; and (iv) applying the proposed strategies to the Brazilian stock market. Experiments were conducted with financial news from the most popular Brazilian online news sources and the results showed: (i) the most appropriate ANN to perform SA in Portuguese is the Convolutional Neural Network; (ii) there is a significant influence of the predominant daily news sentiment in the stock market; and (iii) investment strategies based on Sentiment Analysis can bring profitability both in short and in long term, surpassing the strategies Random Walk and Buy & Hold.
35 citations
TL;DR: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple media formats, like text, image, video and audio.
Abstract: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.
19 citations
References
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Proceedings Article•
26 Oct 2017TL;DR: It is shown that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits.
Abstract: A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.
3,590 citations
TL;DR: This paper reviews significant deep learning related models and methods that have been employed for numerous NLP tasks and provides a walk-through of their evolution.
Abstract: Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.
2,466 citations
TL;DR: This survey paper tackles a comprehensive overview of the last update in this field of sentiment analysis with sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
2,152 citations
Proceedings Article•
25 Jul 2004TL;DR: This project aims to summarize all the customer reviews of a product by mining opinion/product features that the reviewers have commented on and a number of techniques are presented to mine such features.
Abstract: It is a common practice that merchants selling products on the Web ask their customers to review the products and associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds. This makes it difficult for a potential customer to read them in order to make a decision on whether to buy the product. In this project, we aim to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we are only interested in the specific features of the product that customers have opinions on and also whether the opinions are positive or negative. We do not summarize the reviews by selecting or rewriting a subset of the original sentences from the reviews to capture their main points as in the classic text summarization. In this paper, we only focus on mining opinion/product features that the reviewers have commented on. A number of techniques are presented to mine such features. Our experimental results show that these techniques are highly effective.
1,373 citations
"A survey of sentiment analysis in t..." refers methods in this paper
...Another approach was the Hu and Liu hybrid method (Hu and Liu 2004)....
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TL;DR: The emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.
Abstract: Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.
1,153 citations
"A survey of sentiment analysis in t..." refers methods in this paper
...Existing approaches to sentiment analysis can be classified into knowledge-based techniques, statistical methods, and hybrid approaches (Cambria 2016)....
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...(2013), Cambria (2016), Chaturvedi et al....
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