Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes

Rubtsova, Yuliya

doi:10.1007/978-3-319-11716-4_12

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…For the above mentioned tasks we are going to use the following corpora: Paraphraser.ru (Pronoza et al, 2016) for the Russian language paraphrase identification task, Microsoft Research Paraphrase Corpus (Dolan et al, 2004) for the English language paraphrase identification task, Turkish Paraphrase Corpus (Demir et al, 2012) for the Turkish language paraphrase identification task; Russian Twitter Sentiment Corpus (Rubtsova, 2014) for the Russian language sentiment analysis task, Stanford Sentiment Treebank (Socher et al, 2013) for the English language sentiment analysis task; and Stanford Natural Language Inference (Bowman et al, 2015) for the English language natural language inference task.…”

Section: Methodsmentioning

confidence: 99%

Robust to Noise Models in Natural Language Processing Tasks

Malykh

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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There are a lot of noisy texts surrounding a person in modern life. A traditional approach is to use spelling correction, yet the existing solutions are far from perfect. We propose a robust to noise word embeddings model which outperforms existing commonly used models like fasttext and word2vec in different tasks. In addition, we investigate the noise robustness of current models in different natural language processing tasks. We propose extensions for modern models in three downstream tasks, i.e. text classification, named entity recognition and aspect extraction, these extensions show improvement in noise robustness over existing solutions.

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Section: Methodsmentioning

confidence: 99%

Robust to Noise Models in Natural Language Processing Tasks

Malykh

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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“…4 clothing and accessories RuReviews 11 [40] appeared later. RuTweetCorp 12 tweet corpus was automatically labeled based on emoticons [41]. The corpus of Kazakh news Kaggle Russian News Dataset 13 is also known.…”

Section: ) Existing Russian Corporamentioning

confidence: 99%

Cross-Domain Sentiment Analysis Based on Small in-Domain Fine-Tuning

2023

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Significant progress has been made in sentiment analysis over the past few years, especially due to the application of deep neural language models. However, there is a problem of transferability of trained models from one domain to another, especially for less studied languages such as Russian. We propose an approach to build cross-domain sentiment analysis models based on a two-stage procedure: first, we finetune a pre-trained RuBERT language model on a combined non-domain corpus, and then fine-tune this model on a small domain corpus. We conducted large-scale experiments with 30 sentiment annotated corpora across 12 domains. In order to increase the representativeness of news texts with high-quality annotation, we created a novel RuNews corpus, containing 1,823 news articles annotated by sentiment. The results show that finetuning the model using a small number (about several hundred) of annotated domain texts can significantly improve the performance of sentiment analysis for a new domain (on average by 4.6 p.p.). We also obtained the state-of-the-art results for 7 out of 14 test corpora.INDEX TERMS BERT, cross-domain models, neural language models, sentiment analysis

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“…31 Analyzing tweets on these platforms and performing sentiment analysis will provide a great convenience in detecting tweets that will arouse negative feelings in society. Joshi et al 32 36 Glove, 37 TF-IDF, 38 and LDA. 39 Fu et al 40 used Word2Vec to represent datasets as input to the recursive autoencoders.…”

Section: Related Workmentioning

confidence: 99%

“…There are various word embedding techniques in the literature. A few of them: Bag of Words, 35 Word2Vec, 36 Glove, 37 TF‐IDF, 38 and LDA. 39 Fu et al 40 used Word2Vec to represent datasets as input to the recursive autoencoders.…”

Section: Related Workmentioning

confidence: 99%

A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

Aslan

2022

Concurrency and Computation

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Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

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Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes

Cited by 10 publications

References 8 publications

Robust to Noise Models in Natural Language Processing Tasks

Robust to Noise Models in Natural Language Processing Tasks

Cross-Domain Sentiment Analysis Based on Small in-Domain Fine-Tuning

A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

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