A hybrid composite features based sentence level sentiment analyzer

Abstract

<div align="left"><span lang="EN-US">Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction’s accuracy entails sentences and their corresponding training text should fall under the same domain. In this article, we experimentally evaluate four sentiment classifiers, namely Support Vector Machines, Naive Bayes, Logistic Regression and Random Forest. We quantify the quality of each of these models using three real-world datasets that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic movie reviews. Specifically, we study the impact of a variety of natural language processing (NLP) pipelines on the quality of the predicted sentiment orientations. Additionally, we measure the impact of incorporating lexical semantic knowledge captured by WordNet on expanding original words in sentences. Findings demonstrate that the utilizing different NLP pipelines and semantic relationships impacts the quality of the sentiment analyzers. In particular, results indicate that coupling lemmatization and knowledge-based n-gram features proved to produce higher accuracy results. With this coupling, the accuracy of the support vector machine (SVM) classifier has improved to 90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three other classifiers. </span></div>

Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

“…Sentiment analysis aims thus to find the value of emotional polarity in the text so that the polarity in each discussion conversation text can be assessed and classified. The generic approach used to analyze sentiment can be divided into three categories, namely machine learning [18], lexicon-based [19], and hybrid approach [20]. The categories are based on the nature of the model forming.…”

Sentiment Analysis Komentar Berbahasa Bengkulu Menggunakan Long Short-Term Memory (LSTM)

“…Case folding adalah cara sederhana untuk membuat variasi kata menjadi lebih sedikit. Misalnya, jika ada dua kata "Uang" dan "uang" itu akan dikenali sebagai kata yang sama [26].…”

“…Kumpulan data yang diproses sebelumnya digunakan sebagai masukan ke Word2vec [26]. Model arsitektur Word2vec yang digunakan pada penelitian ini adalah model arsitektur Continuous Bag-of-Word (CBOW) dan Skip-gram.…”

Sentimental Analysis and Social Media

“…Additionally, key themes and topics related to the metaverse, such as Facebook, games, platforms, marketing/advertising/PR, recreational business, and science & technology events, are identified. [4] The research examines sentiment analysis techniques, with a specific focus on assessing the efficacy of different approaches in comprehending customer perceptions. It underscores the significance of feature engineering, particularly emphasizing the utilization of ngrams and semantic resources, to enhance the accuracy of sentiment classification.…”