Polarity Classification for Target Phrases in Tweets: A Word2Vec Approach

Rexha, Andi; Kroll, Mark W.; Dragoni, Mauro; Kern, Roman

doi:10.1007/978-3-319-47602-5_40

Cited by 26 publications

(5 citation statements)

References 10 publications

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“…More specifically, word2vec employs neural networks to model the relation between a given word and its context of neighboring words in the given col- This type of model is also referred to as neural language models (Bengio et al, 2003). Recently, word2vec has been shown to be successful in many natural language processing tasks ranging from sentiment analysis (Liang et al, 2015;Ren et al, 2016;Rexha et al, 2016), topic modeling (Bicalho et al, 2017), through document classification (Lilleberg et al, 2015;Yoshikawa et al, 2014) and name entity recognition (Seok et al, 2015;Tang et al, 2014) to machine translation (Freitas et al, 2016;Zou et al, 2013).…”

Section: Continuous-space Modelsmentioning

confidence: 99%

The Influence of Feature Representation of Text on the Performance of Document Classification

2019

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In this paper we perform a comparative analysis of three models for feature representation of text documents in the context of document classification. In particular, we consider the most often used family of models bag-of-words, recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based model that have been rarely considered for representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated the three models and their variants. The results of the empirical comparison show that the commonly used bag-of-words model has performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large

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Section: Continuous-space Modelsmentioning

confidence: 99%

The Influence of Feature Representation of Text on the Performance of Document Classification

2019

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“…These vectors present a fixed size and concentrate information regarding semantic relationships which allows to associate the context to its meaning. As text documents contain different numbers of words, we follow the strategy described by [24], [25] to obtain a single vector representing each text document. For that purpose, the "document vector" is obtained by averaging the word vectors and weighting them according to the frequency of each word.…”

Section: B Feature Extractionmentioning

confidence: 99%

A Comparative Study of Text Document Representation Approaches Using Point Placement-based Visualizations

Carvalho

Borges

2021

Anais Estendidos Da XXXIV Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2021)

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In natural language processing, text representation plays an important role which can affect the performance of language models and machine learning algorithms. Basic vector space models, such as the term frequency-inverse document frequency, became popular approaches to represent text documents. In the last years, approaches based on word embeddings have been proposed to preserve the meaning and semantic relations of words, phrases and texts. In this paper, we focus on studying the influences of different text representations to the quality of the 2D visual spaces (layouts) generated by state-of-art visualizations based on point placement. For that purpose, a visualizationassisted approach is proposed to support users when exploring such representations in classification tasks. Experimental results using two public labeled corpora were conducted to assess the quality of the layouts and to discuss possible relations to the classification performances. The results are promising, indicating that the proposed approach can guide users to understand the relevant patterns of a corpus in each representation.

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“…Once trained, such a model can detect synonymous words or suggest different words for a partial sentence. As the name implies, word2vec represents each specific word with a particular list of numbers called a vector [11].…”

Section: Figure 21mentioning

confidence: 99%

Sentiment Analysis of Tweets and Texts Using Python on Stocks and COVID-19

Jain¹,

Tirth²

2020

International Journal of Computational Intelligence Research

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In a day and age where billions of users regularly use social media and express their opinions online, there is potentially a lot of data that can be harvested and utilized; therefore, it is crucial to develop a quick way to garner data. This study aimed to develop a program using Python to do the same, trying to understand the sentiments of the authors of the text and tweets as well as other additional information about the top tweets and retweets. The main objective of the Twitter Sentiment Analysis is a query-based analysis of tweets. In simple words, Twitter Sentiment Analysis focuses on analyzing the tweets of a specific/particular topic that the user wants to analyze. An extensive collection of such sentiments could leverage to provide a fair reflection of public sentiment towards a specific topic. There are thousands of tweets that can be quickly processed for the sentimental impact, compared to the amount of time it would take a large team of people to complete the same task manually. There are tons of text documents that can also be processed for sentiments in seconds, much faster than just a team of people manually skimming through the text.

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Polarity Classification for Target Phrases in Tweets: A Word2Vec Approach

Cited by 26 publications

References 10 publications

The Influence of Feature Representation of Text on the Performance of Document Classification

The Influence of Feature Representation of Text on the Performance of Document Classification

A Comparative Study of Text Document Representation Approaches Using Point Placement-based Visualizations

Sentiment Analysis of Tweets and Texts Using Python on Stocks and COVID-19

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