Related papers: PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emo…
This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and…
The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by the `NIT-Agartala-NLP-Team'. A dataset of 8879 memes was made available by the task organizers to train and test our models. Our systems include a Logistic…
Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes…
This paper presents a robust solution to the Memotion 3.0 Shared Task. The goal of this task is to classify the emotion and the corresponding intensity expressed by memes, which are usually in the form of images with short captions on…
Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and…
The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre-trained word embedding features, trains them on general regressors and finally…
The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction.…
During online chats, body-language and vocal characteristics are not part of the communication mechanism making it challenging to facilitate an accurate interpretation of feelings, emotions, and attitudes. The use of emojis to express…
We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks -…
Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not…
Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even…
Emotion recognition datasets are relatively small, making the use of the more sophisticated deep learning approaches challenging. In this work, we propose a transfer learning method for speech emotion recognition where features extracted…
Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within…
This paper describes the architecture of our system developed for Task 3 of SemEval-2024: Multimodal Emotion-Cause Analysis in Conversations. Our project targets the challenges of subtask 2, dedicated to Multimodal Emotion-Cause Pair…
We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author…
Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and…
Regressions trained to predict the future activity of social media users need rich features for accurate predictions. Many advanced models exist to generate such features; however, the time complexities of their computations are often…
Emotion recognition from text is a challenging task due to diverse emotion taxonomies, lack of reliable labeled data in different domains, and highly subjective annotation standards. Few-shot and zero-shot techniques can generalize across…
In this study, we present our methodology for two tasks: the Emotional Mimicry Intensity (EMI) Estimation Challenge and the Behavioural Ambivalence/Hesitancy (BAH) Recognition Challenge, both conducted as part of the 8th Workshop and…
Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a…