# Comparing and Combining Sentiment Analysis Methods

arXiv: https://arxiv.org/abs/1406.0032 · HTML: https://mlsanity.com/paper/1406.0032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2014.

## Built on by (most cited first, 6 of 6)

- [SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs](https://mlsanity.com/paper/2110.09454/) (2021)
- [An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets](https://mlsanity.com/paper/2111.03196/) (2021)
- [YouTube Ad View Sentiment Analysis using Deep Learning and Machine Learning](https://mlsanity.com/paper/2205.11082/) (2022)
- [Desenvolvimento de modelo para predicc~ao de cotacc~oes de acc~ao baseada em an'alise de sentimentos de tweets](https://mlsanity.com/paper/2309.06538/) (2023)
- [You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools](https://mlsanity.com/paper/2410.14626/) (2024)
- [Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token](https://mlsanity.com/paper/2605.20192/) (2026)

## Abstract (arXiv)

Several messages express opinions about events, products, and services, political views or even their author's 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.