Combining Labeled Datasets for Sentiment Analysis from Different Domains Based on Dataset Similarity to Predict Electors Sentiment

Santos, Jéssica Soares dos; Paes, Aline; Bernardini, Flávia

doi:10.1109/bracis.2019.00086

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…We divide our architecture into 5 fundamental stages as illustrated in Figure 1. The first step is to identify an appropriate dataset [22] that can yield good results during the training of our models. Next, we proceed to preprocessing [23] with the aim of cleaning our data without damaging the accuracy of the final models.…”

Section: Methodsmentioning

confidence: 99%

Evaluating sentiment analysis and word embedding techniques on Brexit

Moudhich,

Fennan

2024

IJ-AI

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<p>In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.</p><p> </p>

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Section: Methodsmentioning

confidence: 99%

Evaluating sentiment analysis and word embedding techniques on Brexit

Moudhich,

Fennan

2024

IJ-AI

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“…Therefore, we decided to apply a combination of both approaches mentioned above, obtaining instances from both corpora and, simultaneously, adding the labels in which they differ. Previous studies show that it is possible to merge several datasets to train an ML model for sentiment analysis [14].…”

Section: Data Mergingmentioning

confidence: 99%

Merging Datasets for Emotion Analysis

Arriba

Oriol

Franch

2021

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)

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Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated.Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets.Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1score and accuracy.Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset.Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.

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“…The results achieved by them outperformed the existing distance metrics. [Santos et al 2019] evaluated three distance metrics on sentiment analysis in the domain of the 2018 Brazilian Presidential Elections using social media data, like tweets, in Portuguese. These metrics were used for datasets selection with the purpose to merge them, and they showed that choosing similar datasets helps in achieving better results.…”

Section: Related Workmentioning

confidence: 99%

Transfer learning for Twitter sentiment analysis: Choosing an effective source dataset

Guimarães¹,

Carvalho²,

Paes³

et al. 2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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Sentiment analysis on social media data can be a challenging task, among other reasons, because labeled data for training is not always available. Transfer learning approaches address this problem by leveraging a labeled source domain to obtain a model for a target domain that is different but related to the source domain. However, the question that arises is how to choose proper source data for training the target classifier, which can be made considering the similarity between source and target data using distance metrics. This article investigates the relation between these distance metrics and the classifiers’ performance. For this purpose, we propose to evaluate four metrics combined with distinct dataset representations. Computational experiments, conducted in the Twitter sentiment analysis scenario, showed that the cosine similarity metric combined with bag-of-words normalized with term frequency-inverse document frequency presented the best results in terms of predictive power, outperforming even the classifiers trained with the target dataset in many cases.

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Combining Labeled Datasets for Sentiment Analysis from Different Domains Based on Dataset Similarity to Predict Electors Sentiment

Cited by 14 publications

References 23 publications

Evaluating sentiment analysis and word embedding techniques on Brexit

Evaluating sentiment analysis and word embedding techniques on Brexit

Merging Datasets for Emotion Analysis

Transfer learning for Twitter sentiment analysis: Choosing an effective source dataset

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