deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter

2017-08-01 · SEMEVAL 2017 8 · Tzu-Hsuan Yang, Tzu-Hsuan Tseng, Chia-Ping Chen

In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

General Classification Sentiment Analysis Text ClassificationWord Embeddings

deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

2018-06-01 · SEMEVAL 2018 6 · Zi-Yuan Gao, Chia-Ping Chen

This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are…

Classification General Classification Multi-Task Learning Sentiment Analysis

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

2023-04-13 · Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Seid Muhie Yimam, David Ifeoluwa Adelani 외

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEv…

Classification Sentiment Analysis Sentiment Classification zero-shot-classification+1

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

2017-08-01 · SEMEVAL 2017 8 · Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis

This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and c…

BIG-bench Machine Learning General Classification Information Retrieval Sentiment Analysis+2

SemEval-2015 Task 10: Sentiment Analysis in Twitter

2019-12-05 · SEMEVAL 2015 6 · Sara Rosenthal, Saif M. Mohammad, Preslav Nakov, Alan Ritter 외

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of…

Sentiment Analysis

SemEval-2017 Task 4: Sentiment Analysis in Twitter

2019-12-02 · SEMEVAL 2017 8 · Sara Rosenthal, Noura Farra, Preslav Nakov

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the twe…