Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance

• URL: http://arxiv.org/abs/2101.02906v1
• Date: Fri, 8 Jan 2021 08:31:00 GMT
• Title: Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance
• Authors: Anwar Alnawas and Nursal ARICI
• Abstract summary: Social media such as Twitter, Facebook, etc. has led to a generated growing number of comments that contains users opinions. This study will discuss three parameters (Window size, Dimension of vector and Negative Sample) for Arabic sentiment analysis. Four binary classifiers (Logistic Regression, Decision Tree, Support Vector Machine and Naive Bayes) are used to detect sentiment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media such as Twitter, Facebook, etc. has led to a generated growing number of comments that contains users opinions. Sentiment analysis research deals with these comments to extract opinions which are positive or negative. Arabic language is a rich morphological language; thus, classical techniques of English sentiment analysis cannot be used for Arabic. Word embedding technique can be considered as one of successful methods to gaping the morphological problem of Arabic. Many works have been done for Arabic sentiment analysis based on word embedding, but there is no study focused on variable parameters. This study will discuss three parameters (Window size, Dimension of vector and Negative Sample) for Arabic sentiment analysis using DBOW and DMPV architectures. A large corpus of previous works generated to learn word representations and extract features. Four binary classifiers (Logistic Regression, Decision Tree, Support Vector Machine and Naive Bayes) are used to detect sentiment. The performance of classifiers evaluated based on; Precision, Recall and F1-score.

Related papers

• You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools [74.98850427240464]
  We show that sentiment analysis tools disagree on the same dataset. We show that the sentiment tool used for sentiment annotation can even be predicted from its outcome.
  arXiv  Detail & Related papers  (2024-10-18T17:27:38Z)
• Arabic Sentiment Analysis with Noisy Deep Explainable Model [48.22321420680046]
  This paper proposes an explainable sentiment classification framework for the Arabic language. The proposed framework can explain specific predictions by training a local surrogate explainable model. We carried out experiments on public benchmark Arabic SA datasets.
  arXiv  Detail & Related papers  (2023-09-24T19:26:53Z)
• AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses [66.49753193098356]
  We investigate the reason behind the surprising adversarial brittleness of scoring models. Our results indicate that autoscoring models, despite getting trained as "end-to-end" models, behave like bag-of-words models. We propose detection-based protection models that can detect oversensitivity and overstability causing samples with high accuracies.
  arXiv  Detail & Related papers  (2021-09-24T03:49:38Z)
• Sentiment Analysis in Poems in Misurata Sub-dialect -- A Sentiment Detection in an Arabic Sub-dialect [0.0]
  This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool 1.
  arXiv  Detail & Related papers  (2021-09-15T10:42:39Z)
• Arabic aspect based sentiment analysis using BERT [0.0]
  This article explores the modeling capabilities of contextual embeddings from pre-trained language models, such as BERT. We are building a simple but effective BERT-based neural baseline to handle this task. Our BERT architecture with a simple linear classification layer surpassed the state-of-the-art works, according to the experimental results.
  arXiv  Detail & Related papers  (2021-07-28T11:34:00Z)
• Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic [0.0]
  The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. We propose a simple rule-based algorithm for handling the problem; the rules were crafted based on observing many cases of negation.
  arXiv  Detail & Related papers  (2021-07-24T13:12:37Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning [103.0064298630794]
  In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. We propose a progressive self-supervised attention learning approach for attentional ABSA models. We integrate the proposed approach into three state-of-the-art neural ABSA models.
  arXiv  Detail & Related papers  (2021-03-05T02:50:05Z)
• Arabic aspect based sentiment analysis using bidirectional GRU based models [0.0]
  Aspect-based Sentiment analysis (ABSA) accomplishes a fine-grained analysis that defines the aspects of a given document or sentence. We propose two models based on Gated Recurrent Units (GRU) neural networks for ABSA. We evaluate our models using the benchmarked Arabic hotel reviews dataset.
  arXiv  Detail & Related papers  (2021-01-23T02:54:30Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical Theory [0.0]
  This paper is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts.
  arXiv  Detail & Related papers  (2020-07-11T13:34:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.