Enhanced Word Embeddings with Sentiment Contextualized Vectors for Sentiment Analysis

2022 ◽  
pp. 77-86
Author(s):  
Mohammed Kasri ◽  
Marouane Birjali ◽  
Anas El Ansari ◽  
Abderrahim Beni-Hssane
Keyword(s):  
Sentiment Analysis ◽  
Word Embeddings

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 More than Bags of Words: Sentiment Analysis with Word Embeddings

2018 ◽  
Vol 12 (2-3) ◽  
pp. 140-157 ◽  
Author(s):  
Elena Rudkowsky ◽  
Martin Haselmayer ◽  
Matthias Wastian ◽  
Marcelo Jenny ◽  
Štefan Emrich ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Word Embeddings

scholarly journals MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

2019 ◽  
Vol 33 ◽  
pp. 6441-6448 ◽  
Author(s):  
Erfan Ghadery ◽  
Sajad Movahedi ◽  
Heshaam Faili ◽  
Azadeh Shakery
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
English Language ◽  
Online Reviews ◽  
The Internet ◽  
Word Embeddings ◽  
Convolutional Network ◽  
Significant Growth ◽  
Multiple Languages

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

Multi-neural network-based sentiment analysis of food reviews based on character and word embeddings

2020 ◽  
pp. 002072092092849
Author(s):  
Yong Li ◽  
Qingyu Jin ◽  
Min Zuo ◽  
Haisheng Li ◽  
Xiaojun Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Chinese Character ◽  
Semantic Features ◽  
Word Embeddings ◽  
Emotional Information ◽  
Related Sequence ◽  
Active Research ◽  
Multi Neural Network

Sentiment analysis becomes one of the most active research hotspots in the field of natural language processing tasks in recent years. However, the inability to fully and effectively use emotional information is a problem in present deep learning models. A single Chinese character has different meanings in different words, and the character embeddings are combined with the word embeddings to extract more precise meaning information. In this paper, a single Chinese character and word are used as input units to train. Based on BLSTM, the attention mechanism based on vocabulary semantics in food field is introduced to realize distance-related sequence semantic feature extraction. CNN is used to realize semantic sentiment classification of sequence semantic features. Therefore, a model based on multi-neural network for sentiment information extraction and analysis is proposed. Experiments show that the model has excellent characteristics in sentiment analysis and obtains high accuracy and F value.

scholarly journals Comparative study of deep learning models for sentiment analysis

2018 ◽  
Vol 7 (2.14) ◽  
pp. 5726
Author(s):  
Oumaima Hourrane ◽  
El Habib Benlahmar ◽  
Ahmed Zellou
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Word Embeddings ◽  
Human Language ◽  
Learning Models ◽  
Automatic Learning ◽  
Learning Capability ◽  
The Web

Sentiment analysis is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, sentiment analysis and deep learning have been merged as deep learning models are effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.

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