Pollyanna Gonçalves

Comparing and combining sentiment analysis methods

Several messages express opinions about events, products, and services, political views or even their authors emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.

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, 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.

iFeel: a system that compares and combines sentiment analysis methods

Sentiment analysis methods are used to detect polarity in thoughts and opinions of users in online social media. As businesses and companies are interested in knowing how social media users perceive their brands, sentiment analysis can help better evaluate their product and advertisement campaigns. In this paper, we present iFeel, a Web application that allows one to detect sentiments in any form of text including unstructured social media data. iFeel is free and gives access to seven existing sentiment analysis methods: SentiWordNet, Emoticons, PANAS-t, SASA, Happiness Index, SenticNet, and SentiStrength. With iFeel, users can also combine these methods and create a new Combined-Method that achieves high coverage and F-measure. iFeel provides a single platform to compare the strengths and weaknesses of various sentiment analysis methods with a user friendly interface such as file uploading, graphical visualizing, and weight tuning.

Measuring sentiments in online social networks

Sentiment analysis has being used in several applications including the analysis of the repercussion of events in online social networks (OSNs), as well as to summarize public perception about products and brands on discussions on those systems. There are multiple methods to measure sentiments, varying from lexical-based approaches to machine learning methods. Despite the wide use and popularity of some those methods, it is unclear which method is better for identifying the polarity (i.e. positive or negative) of a message, as the current literature does not provide a comparison among existing methods. This comparison is crucial to allow us to understand the potential limitations, advantages, and disadvantages of popular methods in the context of OSNs messages. This work aims at filling this gap by presenting a comparison between 8 popular sentiment analysis methods. Our analysis compares these methods in terms of coverage and in terms of correct sentiment identification. We also develop a new method that combines existing approaches in order to provide the best coverage results with competitive accuracy. Finally, we present iFeel, a Web service which provides an open API for accessing and comparing results across different sentiment methods for a given text.

MEDINDO SENTIMENTOS NO TWITTER POR MEIO DE UMA ESCALA PSICOMÉTRICA

Twitter has become an important social communication mean where users post messages about everything. Certain messages express information about their authors emotional state, which may be useful in developing applications that predict emotional trends of a population or simply to better understand the effects of global and local phenomena in the mood of people. In this study, we adapted a psychometric scale known as the PANAS-X, commonly applied as a questionnaire, to measure the feelings of Twitter users in many social, political and sporting events. Our results suggest that the PANAS-t, our adapted version of the PANAS-X, correctly captures feelings for the events analyzed.