Emotion Recognition of Emoticons Based on Character Embedding

This paper proposes a method for estimating the emotions expressed by emoticons based on a distributed representation of the character meanings of the emoticon. Existing studies on emoticons have focused on extracting the emoticons from texts and estimating the associated emotions by separating them into their constituent parts and using the combination of parts as the feature. Applying a recently developed technique for word embedding, we propose a versatile approach to emotion estimation from emoticons by training the meanings of the characters constituting the emoticons and using them as the feature unit of the emoticon. A cross-validation test was conducted for the proposed model based on deep convolutional neural networks using distributed representations of the characters as the feature. Results showed that our proposed method estimates the emotion of unknown emoticons with a higher F1-score than the baseline method based on character n-grams

Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only

Tension Analysis in Survivor Interviews: A Computational Approach

Tension is an emotional experience that can occur in different contexts. This phenomenon can originate from a conflict of interest or uneasiness during an interview. In some contexts, such experiences are associated with negative emotions such as fear or distress. People tend to adopt different hedging strategies in such situations to avoid criticism or evade questions. In this thesis, we analyze several survivor interview transcripts to determine different characteristics that play crucial roles during tension situation. We discuss key components of tension experiences and propose a natural language processing model which can effectively combine these components to identify tension points in text-based oral history interviews. We validate the efficacy of our model and its components with experimentation on some standard datasets. The model provides a framework that can be used in future research on tension phenomena in oral history interviews

A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts

Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. Majority of such conversations contains code-mixed languages[28]. Additionally, the way used to express thought or communication style also changes from one social media plat-form to another platform (e.g., communication styles are different in twitter and Facebook). These all have increased the complexity of the problem. To solve these problems, we have introduced a unified and robust multi-modal deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset both.The devised system, uses psycho-linguistic features and very ba-sic linguistic features. Our multi-modal deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and Disconnected RNN(with Glove and FastText embedding, both). Finally, the system takes the decision based on model averaging. We evaluated our system on English Code-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained from Kaggle. Experimental results show that our proposed system outperforms all the previous approaches on English code-mixed dataset and uni-lingual English dataset.Comment: 10 pages, 5 Figures, 6 Tables, accepted at CoDS-COMAD 202

“Unknown Symbols”: Online Legal Research in the Age of Emoji

Over the last decade, emoji and emoticons have made the leap from text messaging and social media to legal filings, court opinions, and law review articles. However, emoji and emoticons’ growth in popularity has tested the capability of online legal research systems to properly display and retrieve them in search results, posing challenges for future researchers of primary and secondary sources. This article examines current display practices on several of the most popular online legal research services (including Westlaw Edge, Lexis Advance, Bloomberg Law, Fastcase, HeinOnline, and Gale OneFile LegalTrac), and suggests effective workarounds for researchers

The Role of Preprocessing for Word Representation Learning in Affective Tasks

Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection have drawn a lot of attention in recent years due to a broad range of useful applications in various domains. The main goal of affect detection tasks is to recognize states such as mood, sentiment, and emotions from textual data (e.g., news articles or product reviews). Despite the importance of utilizing preprocessing steps in different stages (i.e., word representation learning and building a classification model) of affect detection tasks, this topic has not been studied well. To that end, we explore whether applying various preprocessing methods (stemming, lemmatization, stopword removal, punctuation removal and so on) and their combinations in different stages of the affect detection pipeline can improve the model performance. The are many preprocessing approaches that can be utilized in affect detection tasks. However, their influence on the final performance depends on the type of preprocessing and the stages that they are applied. Moreover, the preprocessing impacts vary across different affective tasks. Our analysis provides thorough insights into how preprocessing steps can be applied in building an effect detection pipeline and their respective influence on performance

“Unknown Symbols”: Online Legal Research in the Age of Emoji

Over the last decade, emoji and emoticons have made the leap from text messaging and social media to legal filings, court opinions, and law review articles. However, emoji and emoticons’ growth in popularity has tested the capability of online legal research systems to properly display and retrieve them in search results, posing challenges for future researchers of primary and secondary sources. This article examines current display practices on several of the most popular online legal research services (including Westlaw Edge, Lexis Advance, Bloomberg Law, Fastcase, HeinOnline, and Gale OneFile LegalTrac), and suggests effective workarounds for researchers