Sentiment analysis on IMDB using lexicon and neural networks

Shaukat, Zeeshan; Zulfiqar, Abdul Ahad; Xiao, Chuangbai; Azeem, Muhammad; Mahmood, Tariq

doi:10.1007/s42452-019-1926-x

Cited by 66 publications

(35 citation statements)

References 25 publications

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“…Word embedding is a Natural Language Processing approach (NLP) [ 35 ] used to convert words into vector arrangements, with a view to capturing the semantic and syntactic relationship between words, thus simulating human learning of language vocabulary. The problem with encoding is one of great interest in this area of knowledge, which is why we should not just consider superficial forms of a text in order to represent words (e.g., using symbols, characters, word chains, sentences and documents), but also look for significant similarities (e.g., semantic or syntactic ones) between text fragments [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

Jojoa

Castillo-Sánchez

Garcia‐Zapirain

et al. 2021

IJERPH

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The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

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Section: Methodsmentioning

confidence: 99%

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

Jojoa

Castillo-Sánchez

Garcia‐Zapirain

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…In this paper, reviews are labeled as neutral tweets, positive, strong positive, strongnegative, weak-positive, negative, and weak-negative. Additionally, Zeeshan et al [25] had presented a lexicon and ANN-based SA. Here, they utilized the movie review dataset and the dataset consists of two labels namely positive and negative.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

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“…using symbols, characters, word chains, sentences and documents), but also look for significant similarities (e.g. semantic or syntactic ones) between text fragments [34].…”

Section: Word Embeddingmentioning

confidence: 99%

Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation

Jojoa¹,

Castillo-Sánchez²,

Garcia‐Zapirain³

et al. 2021

Preprint

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The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages

show abstract

Sentiment analysis on IMDB using lexicon and neural networks

Cited by 66 publications

References 25 publications

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

An efficient sentiment analysis methodology based on long short-term memory networks

Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation

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