scholarly journals TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news

2017 ◽  
Author(s):  
Leon Rotim ◽  
Martin Tutek ◽  
Jan Šnajder
Keyword(s):  
Sentiment Analysis ◽  
Word Embeddings ◽  
Fine Grained ◽  
Financial News ◽  
Linear Aggregation

Fine-grained financial news sentiment analysis

2017 ◽  
Author(s):  
Bradley Meyer ◽  
Marwan Bikdash ◽  
Xiangfeng Dai
Keyword(s):  
Sentiment Analysis ◽  
Fine Grained ◽  
Financial News ◽  
News Sentiment

Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction

2015 ◽  
Author(s):  
Mo Yu ◽  
Matthew R. Gormley ◽  
Mark Dredze
Keyword(s):  
Relation Extraction ◽  
Word Embeddings ◽  
Fine Grained

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Fine-grained sentiment analysis of online reviews

2015 ◽  
Author(s):  
Yan Wan ◽  
Hongzhurui Nie ◽  
Tianguang Lan ◽  
Zhaohui Wang
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Fine Grained

Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon

2021 ◽  
Author(s):  
Sergio Consoli ◽  
Luca Barbaglia ◽  
Sebastiano Manzan
Keyword(s):  
Sentiment Analysis ◽  
Fine Grained
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