3,507 research outputs found
Topic-dependent sentiment analysis of financial blogs
While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches
Comprehensive Review of Opinion Summarization
The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe
Building a Sentiment Corpus of Tweets in Brazilian Portuguese
The large amount of data available in social media, forums and websites
motivates researches in several areas of Natural Language Processing, such as
sentiment analysis. The popularity of the area due to its subjective and
semantic characteristics motivates research on novel methods and approaches for
classification. Hence, there is a high demand for datasets on different domains
and different languages. This paper introduces TweetSentBR, a sentiment corpora
for Brazilian Portuguese manually annotated with 15.000 sentences on TV show
domain. The sentences were labeled in three classes (positive, neutral and
negative) by seven annotators, following literature guidelines for ensuring
reliability on the annotation. We also ran baseline experiments on polarity
classification using three machine learning methods, reaching 80.99% on
F-Measure and 82.06% on accuracy in binary classification, and 59.85% F-Measure
and 64.62% on accuracy on three point classification.Comment: Accepted for publication in 11th International Conference on Language
Resources and Evaluation (LREC 2018
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
In the last few years thousands of scientific papers have investigated
sentiment analysis, several startups that measure opinions on real data have
emerged and a number of innovative products related to this theme have been
developed. There are multiple methods for measuring sentiments, including
lexical-based and supervised machine learning methods. Despite the vast
interest on the theme and wide popularity of some methods, it is unclear which
one is better for identifying the polarity (i.e., positive or negative) of a
message. Accordingly, there is a strong need to conduct a thorough
apple-to-apple comparison of sentiment analysis methods, \textit{as they are
used in practice}, across multiple datasets originated from different data
sources. Such a comparison is key for understanding the potential limitations,
advantages, and disadvantages of popular methods. This article aims at filling
this gap by presenting a benchmark comparison of twenty-four popular sentiment
analysis methods (which we call the state-of-the-practice methods). Our
evaluation is based on a benchmark of eighteen labeled datasets, covering
messages posted on social networks, movie and product reviews, as well as
opinions and comments in news articles. Our results highlight the extent to
which the prediction performance of these methods varies considerably across
datasets. Aiming at boosting the development of this research area, we open the
methods' codes and datasets used in this article, deploying them in a benchmark
system, which provides an open API for accessing and comparing sentence-level
sentiment analysis methods
- âŠ