Emerging Sentiment Language Model for Emotion Detection
Authors: Anastasia Giachanou, Francisco Rangel, Fabio Crestani, Paolo Rosso (Università della Svizzera italiana, Universitat Politècnica de València, Autoritas Consulting S.A., 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper we present an approach for joy, anger and neutral emotions detection based on an emerging sentiment language model. We propose an approach that can detect specific emotions from positive, neutral and negative sentiments and which favors the tweets that occur at recent sentiment spikes. Our results suggest that our approach can effectively detect joy, neutral and anger emotions and that it performs better compared to the baselines.

Introduction
Recent years have seen the emergence of social media that enable people to share their thoughts and opinions in an easy and fast way. Opinions posted on social media are very useful to understand what people think and how they feel about a specific entity (e.g. a product, a person, a company etc.). For example, companies can mine users' opinions on a product that has just been released to understand if the users are satisfied or not and act accordingly. Therefore, the automatic detection of emotions and sentiments from text has attracted a lot of research interest AUTHOR. Although sentiment and emotion analysis share some similarities, they are two different problems AUTHOR. Sentiment analysis focuses on understanding the sentiment polarity (positive, neutral, negative) of a text AUTHOR whereas emotion analysis refers to its affectual attitude such as anger, joy, fear etc. AUTHOR. Most of the previous work have tried to predict sentiments from data annotated with sentiments and emotions from data annotated with emotions. However, preliminary experiments showed that some emotions are related to sentiments. Specifically, negative sentiment is related to anger, positive sentiment to joy and text with no emotion (neutral emotion) to text with no sentiment (neutral sentiment). In addition, public sentiment towards a specific entity changes over time and in some cases sentiment spikes may occur. Sentiment spikes occur when a large amount of documents of a specific sentiment is posted AUTHOR. The documents that occur at sentiment spikes usually refer to a topic or event that attracted a lot of attention and therefore they can be very helpful for sentiment and emotion analysis. To this end, in this study we propose to incorporate information from the documents that have occurred at sentiment spikes to improve the performance of the emotion analysis task. In this paper, we focus on Twitter and we propose the emerging Sentiment Language Model (emerging-SLM) approach which favors tweets that occur at recent sentiment spikes with the aim to predict joy, anger and neutral emotions from positive, negative and neutral sentiments respectively. We test our approach on a collection of tweets that spans over nine days and we show that the emerging-SLM performs better compared to both state-of-art Sentiment Language Model (SLM) and to a random Sentiment Language Model (random-SLM).

Conclusion
In this paper, we proposed the emerging-SLM approach to detect joy, neutral and anger emotions from positive, neutral and negative sentiments respectively. Emerging-SLM favors tweets that occur at recent sentiment spikes. The results showed that our approach performs better compared to both SLM and random-SLM and can be effectively applied to detect specific emotions. In future we plan to explore if there is any effect of the temporal bins size on the emotion detection performance and if sentiment language models can be used to detect also other emotions such as fear and surprise.