There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

Sentiment of Emojis.

As people increasingly use emoticons in text in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons significantly improves sentiment classification accuracy. This indicates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment.

Exploiting emoticons in sentiment analysis

A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.

/pdf/a-systematic-review-of-emoji-current-research-and-future-46phb8j6fk.pdf

A Systematic Review of Emoji: Current Research and Future Perspectives.

The objective of this article is to present a hybrid approach to the Sentiment Analysis problem at the sentence level. This new method uses natural language processing (NLP) essential techniques, a sentiment lexicon enhanced with the assistance of SentiWordNet, and fuzzy sets to estimate the semantic orientation polarity and its intensity for sentences, which provides a foundation for computing with sentiments. The proposed hybrid method is applied to three different data-sets and the results achieved are compared to those obtained using Naive Bayes and Maximum Entropy techniques. It is demonstrated that the presented hybrid approach is more accurate and precise than both Naive Bayes and Maximum Entropy techniques, when the latter are utilised in isolation. In addition, it is shown that when applied to datasets containing snippets, the proposed method performs similarly to state of the art techniques.

A hybrid approach to the sentiment analysis problem at the sentence level

Choosing the right emoji to visually complement or condense the meaning of a message has become part of our daily life. Emojis are pictures, which are naturally combined with plain text, thus creating a new form of language. These pictures are the same independently of where we live, but they can be interpreted and used in different ways. In this paper we compare the meaning and the usage of emojis across different languages. Our results suggest that the overall semantics of the subset of the emojis we studied is preserved across all the languages we analysed. However, some emojis are interpreted in a different way from language to language, and this could be related to socio-geographical differences.

/pdf/how-cosmopolitan-are-emojis-exploring-emojis-usage-and-4tbi76336q.pdf

How Cosmopolitan Are Emojis?: Exploring Emojis Usage and Meaning over Different Languages with Distributional Semantics

Sentiment analysis refers to retrieving an author's sentiment from a text. We analyze the differences that occur in sentiment scoring across languages. We present our experiments for the Dutch and English language based on forum, blog, news and social media texts available on the Web, where we focus on the differences in the use of a language and the effect of the grammar of a language on sentiment analysis. We propose a multilingual pipeline for evaluating how an author's sentiment is conveyed in different languages. We succeed in correctly classifying positive and negative texts with an accuracy of approximately 71% for English and 79% for Dutch. The evaluation of the results shows however that usage of common expressions, emoticons, slang language, irony, sarcasm, and cynicism, acronyms and different ways of negation in English prevent the underlying sentiment scores from being directly comparable.

/pdf/sentiment-analysis-with-a-multilingual-pipeline-2rjg122wkt.pdf

Sentiment analysis with a multilingual pipeline

As consumers nowadays generate increasingly more content describing their experiences with, e.g., products and brands in various languages, information systems monitoring a universal, language-independent measure of people's intended sentiment are crucial for today's businesses. In order to facilitate sentiment analysis of user-generated content, we propose to map sentiment conveyed by unstructured natural language text to universal star ratings, capturing intended sentiment. For these mappings, we consider a monotonically increasing step function, a naive Bayes method, and a support vector machine. We demonstrate that the way in which natural language reveals intended sentiment differs across our datasets of Dutch and English texts. Additionally, the results of our experiments on modelling the relation between conveyed sentiment and intended sentiment suggest that language-specific sentiment scores can separate universal classes of intended sentiment from one another to a limited extent.

Lexicon-based sentiment analysis by mapping conveyed sentiment to intended sentiment

The abundance of sentiment-carrying user-generated content renders automated cross-language informationmonitoring tools crucial for today’s businesses. In order to facilitate cross-language sentiment analysis, we propose to compare the sentiment conveyed by unstructured text across languages through universal star ratings for intended sentiment. We demonstrate that the way natural language reveals people’s intended sentiment differs across languages. The results of our experiments with respect to modeling this relation for both Dutch and English by means of a monotone increasing step function mainly suggest that language-specific sentiment scores can separate universal classes of intended sentiment from one another to a limited extent.

Daniella Bal

Papers

Exploiting emoticons in sentiment analysis

Sentiment analysis with a multilingual pipeline

Lexicon-based sentiment analysis by mapping conveyed sentiment to intended sentiment

Towards Cross-Language Sentiment Analysis through Universal Star Ratings