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Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

 | 
Roberto Basili
, 
Malvina Nissim
, 
Giorgio Satta

Emerging Sentiment Language Model for Emotion Detection

Anastasia Giachanou, Francisco Rangel, Fabio Crestani et Paolo Rosso

Résumé

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.

In questo articolo presentiamo un approccio per rilevare gioia, rabbia e emozione neutra basato su un modello di sentiment analysis emergente. Proponiamo un approccio in grado di rilevare emozioni specifiche da sentimenti positivi, neutri e negativi e che favorisca i tweets che si verificano nei picchi recenti di sentimento. I risultati suggeriscono che il nostro approccio può rilevare efficacemente le emozioni di gioia, rabbia e neutra che ottiene migliori risultati delle baseline.

Texte intégral

1 Introduction

1Recent 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 (Tang et al., 2015; Mohammad, 2015).

2Although sentiment and emotion analysis share some similarities, they are two different problems (Munezero et al., 2014). Sentiment analysis focuses on understanding the sentiment polarity (positive, neutral, negative) of a text (Pang and Lee, 2008) whereas emotion analysis refers to its affectual attitude such as anger, joy, fear etc. (Mohammad, 2015). 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).

3In 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 (Giachanou et al., 2016). 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.

4In 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).

2 Related Work

5Sentiment and emotion analysis have both attracted much research attention (Mohammad, 2015; Giachanou and Crestani, 2016). The main difference between the two problems is that sentiment refers to the polarity (e.g. positive, neutral, negative) whereas emotion refers to the affectual attitude that is anger, joy, fear etc. (Mohammad, 2015).

6Sentiment analysis has attracted a tremendous research attention over the last years. The proposed approaches can be roughly classified as learning and lexicon based. The lexicon based approaches are typically unsupervised and use lists of words (e.g. good,bad) whose presence implies a specific sentiment polarity (Turney, 2002; Taboada et al., 2011). The learning based approaches rely on a number of features, usually extracted from text, to build a classifier which is then used to annotate unlabeled text as positive, negative or neutral (Pang et al., 2002). More recently, researchers have proposed deep learning approaches to learn sentiment specific word embeddings (Tang et al., 2014) or semantic representations of user and products (Tang et al., 2015) to address sentiment analysis. A thorough review on opinion retrieval and sentiment analysis can be found in Pang and Lee (2008) whereas Giachanou and Crestani (2016) focused on Twitter sentiment analysis.

7With regards to emotion analysis, Mohammad (2012) considered hashtags that refer to an emotion (e.g., #anger, #surprise) to create a collection for emotion analysis and showed that these hashtag annotations matched with the annotations of trained judges. Roberts et al. (2012) extended the list of the six Ekman’s basic emotions (joy, anger, fear, sadness, surprise, disgust) (Ekman, 1992) with an additional emotion (love) and created a series of binary SVM emotion classifiers. Also, other researchers have used sentiments or emotions to address other tasks such as irony detection (Farías et al., 2016) or author profiling (Rangel and Rosso, 2016).

8In general, language models have been used for text classification problems (Bai et al., 2004). With regards to sentiment analysis, Liu et al. (2012) used manually annotated data to train a language model and then applied smoothing using noisy emoticon data. There are also few works that have considered sentiment dynamics. Bollen et al. (2011) used a psychometric instrument to extract and analyze different moods (tension, depression, anger, vigor, fatigue, confusion) detected in tweets and found that the mood level is correlated to cultural, political and other world global events while An et al. (2014) combined sentiment analysis, data mining and time series methods to track sentiment regarding climate change from Twitter feeds. However, our work is different because we use temporal information to favor documents that were posted recently and attracted a lot of attention with the aim to improve the performance of detecting specific emotions.

3 Methodology

9Language Models (LMs) that are widely used in Information Retrieval (IR) and Natural Language Processing (NLP) fields assign probabilities to sequences of words (Ponte and Croft, 1998). The most typical scenario in IR consists in generating a Language Model (LM) for each document and then estimating the likelihood that the query was generated by each document. The documents then can be ranked based on the likelihoods. For a classification problem, we first aggregate all the documents of each specific class and then we estimate the likelihood that a new document is generated from each of the estimated language models. The new document can be annotated with the class for which it has the maximum likelihood.

More formally, let Image 10000000000000160000000E7B2DCCB2.jpg, Image 10000000000000120000000C6E7A7B97.jpg, Image 10000000000000160000000CEEB24B3C.jpg be the LMs for the positive, neutral and negative classes respectively. Given a test tweet d we can detect its emotion class (joy, anger, neutral) c’ as:

Image 1000000000000098000000332379F718.jpg

where |d| is the number of words in tweet d and Image 100000000000003900000013D5B96D7B.jpg is a multinomial distribution estimated from the LM of class c (positive, negative, neutral).

10To estimate the distributions we use the Maximum Likelihood Estimate (MLE) which computes the probabilities as follows:

Image 10000000000000AA0000002EEFA82CF3.jpg

where n(t, c) is the number of times that the term t appears in the collection of documents of class c and |Vc| is the size of the vocabulary of class c.

11The emerging-SLM combines two different LMs to estimate the probabilities of the terms. The first LM is based on all the tweets of the collection excluding those that occur at recent sentiment spikes whereas the second is based on tweets that occurred at those recent sentiment spikes. Formally, the distributions of the terms using the emerging-SLM are estimated as:

Image 100000000000014D00000011B1D12716.jpg

where Image 10000000000000130000000CCA781EAC.jpg is the LM for the class c, Image 100000000000005100000011259C7618.jpg is the probability of that term t appear in the recent sentiment spikes of the class c, Image 1000000000000055000000114CCBC97C.jpg is the probability of that term t appear in the class c and < is the parameter that determines the importance of each LM for the final estimation. Here we should note that Image 100000000000005500000011E9A32844.jpg is calculated after we excluded the tweets that occurred at sentiment spikes.

One common issue with the LMs is that they assign zero probabilities to terms that do not appear in the training data. To overcome this problem, we apply Jelinek-Mercer smoothing that assigns nonzero probabilities to unseen terms (Zhai and Lafferty, 2004). Jelinek-Mercer smoothing refers to a linear interpolation of the MLE and the collection language model Image 10000000000000330000001180A0A842.jpg and can be defined as:

Image 10000000000001150000001134C0EC2C.jpg

where the collection language model is estimated using the maximum likelihood estimate of the whole collection.

12To detect the sentiment spikes, we measure the evolution of each sentiment as rt,s = Nt,s/Nt where Nt is the number of documents that express the sentiment s posted at time t and is the total number of documents posted at time t. Figure 1 shows an example of negative spikes that occurred while tracking the sentiment towards Michelle Obama.

4 Experimental Setup

13In this section we describe the experimental details of our study that include the description of the dataset, the baselines we used and the experimental settings.

4.1 Dataset

14Our collection contains 25,588 tweets about Michelle Obama and spans from to July 2, 2015. To annotate the collection, we used the Crowdflower platform1. Tweets were annotated with regards to sentiment and emotion. For sentiments, annotators could choose among {positive, no sentiment, negative} whereas for emotions they could choose among {anger, fear, sadness, disgust, surprise, happiness, no emotion}. Each tweet was annotated by three different workers.

Figure 1: Negative spikes that occurred while tracking the sentiment towards Michelle Obama

Image 10000000000002D3000001471DF90B00.jpg

15To optimize the annotation process and obtain more labels we applied a type of distant supervision, which is a popular technique for obtaining more labels for the data (Go et al., 2009). In our study we used the similarity between the tweets because a large amount of tweets are posted again (retweets). Therefore, first we ranked the tweets by how may times they were retweeted and then we collected annotations for the most popular ones. Next, we disseminated the labels to the rest of the tweets using a similarity threshold set to 0.8. We used cosine similarity to measure the similarity between two tweets.

16For all the results reported to this study, we used 429 tweets as a test set which were posted on July 2, 2015. We kept the test and training data always separated.

4.2 Baselines

17We used two different baselines to compare the performance of our approach. The first baseline (SLM) is based on sentiment language models and was built from all the data without favoring tweets that occurred at spikes (i.e. λ = 1.0). The second baseline is the random-SLM approach. In this case, instead of using tweets from recent sentiment spikes, we randomly chose tweets from the whole collection. To build the random LM we select as many tweets as those used to build the LM of sentiment spikes. To evaluate the statistical significance of differences we used the McNemar test.

4.3 Experimental Settings

18For pre-processing, we removed URLs, mentions, punctuation and the entity-related terms Michelle and Obama. For the experiments we used only unigrams. To overcome the problem of assigning zero probabilities to unseen terms we used JelinekMercer smoothing with μ = 0.1.

19To model the evolution of sentiment and detect any sentiment spikes we split the data hourly. In addition, we defined temporal bins with the size of 8 hours. For the emerging-SLM we detected all the sentiment spikes that occurred in the last two days. To detect the spikes, we used the peakutils2 package setting the threshold to 0.8.

20Finally, to tune the λ parameter, we used crossvalidation on a rolling basis. Following this approach, we used data published on the first temporal bin as training and data of the second temporal bin as test. Next, data from the first and second temporal bins were used for training and data from the third temporal bin as test and so on. In other words, when we set the number of bins to 9, it means that we were using 3 days as training data (i.e. 8 hours * 9 bins = 72 hours) and one bin as test data. The test bin was always the adjacent temporal bin. The first setup included 3 days, since we wanted to have enough data to build the SLMs. After this process we estimated the best parameters using the average performance.

5 Results

21Figure 2 shows the performance with regards to the F1-measure for the task of emotion detection using the emerging-SLM on the training data for the different parameters of λ. We show the results for six different temporal bins for reasons of clarity. From this figure, we observe that there is a performance improvement as the λ parameter increases.

22Table 1 shows the performance with regards to F1-measure for the task of emotion detection using the emerging-SLM, the SLM and the randomSLM approaches. From the results we observe that the emerging-SLM performs better compared to SLM and random-SLM for all the temporal bins. Also, most of the differences are significant. These results are very important because they show that favoring tweets that have occurred at recent sentiment spikes is very useful. Also, the improvement over the random-SLM validates further this assumption. The results are also shown on Figure 3 for an easier comparison.

Figure 2: Performance of the emerging-SLM on the training data with regards to F1-measure for different λ parameters for six different bins

Image 10000000000002C0000001C407A298F2.jpg

Figure 3: Performance measure with regards to F1-measure using different temporal bins

Image 10000000000002C0000001C26FD6A709.jpg

6 Conclusions and Future Work

23In 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.

24In 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.

Table 1: Performance results over the test data using different size for the temporal bins of the emerging-SLM, SLM and random-SLM approaches. A star () means there is a statistically significant difference between the emergingSLM and SLM (p<0.05). A † indicates a significance difference between the emerging-SLM and random-SLM (p<0.05)

Bins

emerging-SLM

SLM

random-SLM

9

0.6352*†

0.6419

0.5473

10

0.6352*†

0.6213

0.5725

11

0.6358*†

0.5842

0.6303

12

0.6358*†

0.5841

0.5781

13

0.6419*†

0.5980

0.5803

14

0.6415*†

0.6012

0.5922

15

0.6493*†

0.6078

0.5780

16

0.6551*†

0.5914

0.5821

17

0.6064*†

0.5961

0.5853

18

0.6034*†

0.5828

0.5798

19

0.5987*†

0.5751

0.5797

20

0.5987*†

0.5751

0.5750

21

0.5792*†

0.5564

0.5683

22

0.5855*†

0.5557

0.5796

23

0.5837*†

0.5604

0.5804

24

0.6311*†

0.5651

0.5805

Acknowledgement

25The work of the first author was partially funded by the Swiss National Science Foundation (SNSF) under the project OpiTrack.

26The work of the last author was in the framework of the Spanish MINECO research project SomEMBED (TIN2015-71147-C2-1-P).

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Auteurs

Faculty of Informatics, Università della Svizzera italiana (USI), Lugano, Switzerland – anastasia.giachanou@usi.ch

PRHLT Research Center, Universitat Politècnica de València, Spain – Autoritas Consulting, S.A., Spain – francisco.rangel@autoritas.es

Faculty of Informatics, Università della Svizzera italiana (USI), Lugano, Switzerland – fabio.crestani@usi.ch

PRHLT Research Center, Universitat Politècnica de València, Spain – prosso@dsic.upv.es

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