An algorithm and method for sentiment analysis using the text and emoticon

Ullah, Mohammad Aman; Marium, Syeda Maliha; Begum, Shamim Ara; Dipa, Nibadita Saha

doi:10.1016/j.icte.2020.07.003

Cited by 81 publications

(38 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While building LDA models we analyzed that to extract the most relevant topic distributions, careful text preprocessing is very necessary as it ultimately impacts the model performance. In this regard, we leveraged lemmatization instead of stemming in our text preprocessing, because it gives or reduced the words into their root form with the contextual meaning ( Ullah et al, 2020 ). This framework is flexible in a way that it only requires text contents and categories in which you want to classify the data, and this framework is capable to be applied to different domains, like opinion mining, social media sentiment classification, user reviews classification, customer complaints classification government organization.…”

Section: Discussionmentioning

confidence: 99%

“…Sentiment analysis is a typical classification problem, used in various ways, some researchers apply sentiment analysis on reviews of movies ( Shen et al, 2020 ). Many deep learning and natural language processing techniques are proposed for sentiment analysis ( Ullah et al, 2020 ). For sentiment classification in our article, we have considered a public dataset that we collected from the data repository Kaggle.…”

Section: Proposed Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions

Wahid

Shi

Gao

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features’ cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generated from that data. We explored how the representation based on topics affect the classification performance by applying supervised classification algorithms. Additionally, we did careful evaluation on two types of datasets and compared them with baseline approaches without topic distributions and other comparable methods. The results show that our framework performs significantly better in terms of classification performance compared to the baseline(without T2F) approaches and also yields improvement in terms of F1 score compared to other compared approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Proposed Frameworkmentioning

confidence: 99%

Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions

Wahid

Shi

Gao

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…They used TF-IDF and Unigram with Sentiwordnet for training feature vectors of the input dataset, before classifying them using different techniques such as Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). In [45], Ullah et al proposed an algorithm and method for sentiment analysis using both text and emoticons. The two modes of data were analyzed in combination and separately with machine learning and deep learning algorithms to find sentiments from Twitter-based airline data using several features such as TF-IDF, N-gram and emoticon lexicons.…”

Section: Term Frequency-inverse Document Frequencymentioning

confidence: 99%

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Chiny¹,

Chihab²,

Bencharef³

et al. 2021

IJACSA

View full text Add to dashboard Cite

Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rulebased sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and F1 score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.

show abstract

“…As first step, data cleaning exhibit to clean unnecessary reviews from selected dataset [17] [18]. Data preprocessing perform to remove all missing values, remove stop words, tokenization, unwanted symbols, digits and URL tags [31].…”

Section: A Preprocessed Textsmentioning

confidence: 99%