Crowdsourcing a Word-Emotion Association Lexicon

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word-emotion and word-polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher inter-annotator agreement than that obtained by asking if a term evokes an emotion

A lexicon based method to search for extreme opinions

Studies in sentiment analysis and opinion mining have been focused on many aspects related to opinions, namely polarity classification by making use of positive, negative or neutral values. However, most studies have overlooked the identification of extreme opinions (most negative and most positive opinions) in spite of their vast significance in many applications. We use an unsupervised approach to search for extreme opinions, which is based on the automatic construction of a new lexicon containing the most negative and most positive wordsS

Hedges and Boosters as Modality Markers: An Analysis of Nigerian and American Editorials

Many studies have been carried out on the use of hedges and boosters as persuasive strategies, but little is known about their employment when texts such as editorials are compared cross culturally. This study comparatively examined the employment of modality markers to express doubt and conviction in Nigerian and American editorials. Farrokhi and Emami’s (2008) classification of hedges and boosters was employed to analyze twenty editorials selected from two Nigerian newspapers and two American newspapers. Findings reveal that both sets of editorial writers made use of hedges and boosters a lot in their writings. However, lexical verbs were not employed as boosters in the analyzed editorials. The fact that the Nigerian editorial writers as ESL writers equally made great use of hedges and boosters implies that in texts such as editorials, writers from different cultures equally employ the same linguistic devices to express doubt and conviction. &nbsp

Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve

Sentiment-Based Assessment Of Electronic Mixed-Motive Communication - A Comparison Of Approaches

In this paper, we seek to analyse specific types of bilateral electronic communication processes, namely such processes where there is a distinction between individual goals of the communicating parties and their joint goals. We argue that there exists a distinction between successful and unsuccessful processes. This distinction is manifest in the communication patterns used by the participants. Sentiment analysis can enable researchers to identify these distinctions automatically, based on a classification model previously trained for the exact type of communication process. This paper discusses an adaption of sentiment-based techniques for the domain of electronic business negotiations

An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aims at transferring the results and methods to discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and disgust. For instance, it is not straight-forward to interpret which emotion the phrase "not happy" expresses. With this paper, we aim at obtaining a better understanding of such modifiers in the context of emotion-bearing words and their impact on document-level emotion classification, namely, microposts on Twitter. We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification. In addition, we build a term weighting approach based on the different modifiers into a lexical model for the analysis of the semantics of modifiers and their impact on emotion meaning. We show that amplifiers separate emotions expressed with an emotion- bearing word more clearly from other secondary connotations. Downtoners have the opposite effect. In addition, we discuss the meaning of negations of emotion-bearing words. For instance we show empirically that "not happy" is closer to sadness than to anger and that fear-expressing words in the scope of downtoners often express surprise.Comment: Accepted for publication at The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), https://dsaa2018.isi.it

Sentiment and Authority Analysis in Conversational Content

This paper deals with mining conversational content from the social media. It focused on two issues: opinion and emotion classification and identification of authoritative reviewers. The paper also describes applications representing the results obtained in the given areas. Authority identification can be used by organizations to search for experts in their specific areas to employ them. The opinion and emotion analysis can be useful for providing decision-making support

Opinion mining: Reviewed from word to document level

International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

Enhanced Topic-Based Modeling for Twitter Sentiment Analysis

abstract: In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models.Dissertation/ThesisMasters Thesis Computer Science 201

El diseño de materiales para el desarrollo de la Competencia Intercultural Comunicativa en el aula de ILE

This present paper explores, analyses, proves and tries to provide a solution to the patent need for Intercultural Communicative Competence Development that a group of 56 students of first year of post-compulsory education portray. Thus, trough the convergence of different fields of study ranging from Intercultural Communicative Competence, Communicative Language Teaching and Second Language Acquisition, this document presents a needs analysis and a subsequent pedagogical intervention proposal that aims to erode the existing prejudices in young students today and to promote intercultural interaction by raising their awareness and by providing them with the necessary tools to reflect on themselves and on the others in a more tolerant, thoughtful and communicative way. The pedagogical intervention proposal has been nominated as “Ten Mini-Pills for Intercultural Communicative Competence Development” in which students are faced with concrete examples of the elements that shape other cultures so as for them to be able to deconstruct their cosmovision and thus integrate the reality of others. The “Ten Mini-Pills for Intercultural Communicative Competence Development” have been designed in the light of adaptability and easy implementation, so as for them to not only meet the needs of this concrete group of students but of as many as possible. <br /