User emotion identification in twitter using specific features: hashtag, emoji, emoticon, and adjective term

TL;DR: Two approaches that utilize emojis are proposed to obtain the sentiment and emotions contained in social media reactions, and visualizations that focus on space, time, and topic are applied to Twitter reactions in the example case of Brexit.

Abstract: Background: Cancer and its treatment can significantly impact the short- and long-term psychological well-being of patients and families. Emotional distress and depressive symptomatology are often associated with poor treatment adherence, reduced quality of life, and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective strategies to build resilience and enhance coping. However, in-person support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition (eg, fatigue, pain) is another. Emerging evidence supports the effectiveness of online support groups in reducing access barriers. Text-based and professional-led online support groups have been offered by Cancer Chat Canada. Participants join the group discussion using text in real time. However, therapist leaders report some challenges leading text-based online support groups in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or red flags for distress can sometimes be missed. Recent advances in artificial intelligence such as deep learning–based natural language processing offer potential solutions. This technology can be used to analyze online support group text data to track participants’ expressed emotional distress, including fear, sadness, and hopelessness. Artificial intelligence allows session activities to be monitored in real time and alerts the therapist to participant disengagement. Objective: We aim to develop and evaluate an artificial intelligence–based cofacilitator prototype to track and monitor online support group participants’ distress through real-time analysis of text-based messages posted during synchronous sessions. Methods: An artificial intelligence–based cofacilitator will be developed to identify participants who are at-risk for increased emotional distress and track participant engagement and in-session group cohesion levels, providing real-time alerts for therapist to follow-up; generate postsession participant profiles that contain discussion content keywords and emotion profiles for each session; and automatically suggest tailored resources to participants according to their needs. The study is designed to be conducted in 4 phases consisting of (1) development based on a subset of data and an existing natural language processing framework, (2) performance evaluation using human scoring, (3) beta testing, and (4) user experience evaluation. Results: This study received ethics approval in August 2019. Phase 1, development of an artificial intelligence–based cofacilitator, was completed in January 2020. As of December 2020, phase 2 is underway. The study is expected to be completed by September 2021. Conclusions: An artificial intelligence–based cofacilitator offers a promising new mode of delivery of person-centered online support groups tailored to individual needs.

TL;DR: This research aims to classify the tax complaint on twitter automatically by using text mining, and the experimental results show the value of SVM, Naive Bayes and Decision Tree, respectively, are 89.3%, 85.6% and 76.9% respectively.

TL;DR: Investigation of the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place suggests that emoj is suitable for identifying less obvious characteristics and the sense of a place.

TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.

TL;DR: The theory of fuzzy sets is introduced into the K-nearest neighbor technique to develop a fuzzy version of the algorithm, and three methods of assigning fuzzy memberships to the labeled samples are proposed.

TL;DR: The manual annotation process and the results of an inter-annotator agreement study on a 10,000-sentence corpus of articles drawn from the world press are presented.

TL;DR: The experiments demonstrate that a combination of unigrams, big rams, sentiment/emotion-bearing words, and parts-of-speech information is most effective for gleaning emotions.