Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features

Kramer, Jared; Gordon, Clara

doi:10.3115/v1/s14-1003

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Parsing and generation are thus performed using already existing DELPH-IN tools. DMRS has already been used in other systems for prepositional phrase attachment disambiguation [36], for machine translation [37], for question generation [38], for evaluating multimodal deep learning models [39], and for sentiment analysis [40].…”

Section: Grass Theoretical Foundationsmentioning

confidence: 99%

GRASS: A Syntactic Text Simplification System based on Semantic Representations

Hijazi¹,

Espinasse²,

Gala³

2022

Data Science and Machine Learning

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Automatic Text Simplification (ATS) is the process of reducing a text's linguistic complexity to improve its understandability and readability while maintaining its original information, content, and meaning. Several text transformation operations can be performed such as splitting a sentence into several shorter sentences, substitution of complex elements, and reorganization. It has been shown that the implementation of these operations essentially at a syntactic level causes several problems that could be solved by using semantic representations. In this paper, we present GRASS (GRAph-based Semantic representation for syntactic Simplification), a rulebased automatic syntactic simplification system that uses semantic representations. The system allows the syntactic transformation of complex constructions, such as subordination clauses, appositive clauses, coordination clauses, and passive forms into simpler sentences. It is based on graph-based meaning representation of the text expressed in DMRS (Dependency Minimal Recursion Semantics) notation and it uses rewriting rules. The experimental results obtained on a reference corpus and according to specific metrics outperform the results obtained by other state of the art systems on the same reference corpus.

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Section: Grass Theoretical Foundationsmentioning

confidence: 99%

GRASS: A Syntactic Text Simplification System based on Semantic Representations

Hijazi¹,

Espinasse²,

Gala³

2022

Data Science and Machine Learning

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“…where m is the number of features, a i is the value of the i th feature, L is the set of all class labels, c represents the value that the class variable can take, and f i is the frequency count of the word a i in a document d. The prior probability p(c) and the conditional probability p(a i |c) are generally estimated by (2) and 3, respectively.…”

Section: Related Work a Multinomial Naïve Bayes Text Classifiermentioning

confidence: 99%

“…Automatic text classification is used to automatically assign a textual document to a pre-specified set of classes, which can help people retrieve, query, and utilize information. Current common text classification algorithms include [8]: Naïve Bayes [2], K-nearest neighbors [3], decision trees [4], support vector machine (SVM) [5], and recent deep learning methods such as convolutional neural networks (CNNs) [6], recurrent neural networks (RNNs), and so on [7], [28].…”

Section: Introductionmentioning

confidence: 99%

Class-Specific Deep Feature Weighting for Naïve Bayes Text Classifiers

Ruan

et al. 2020

IEEE Access

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Feature weighting is used to alleviate the conditional independence assumption of Naïive Bayes text classifiers and consequently improve their generalization performance. Most traditional feature weighting algorithms use general feature weighting, which assigns the same weight to each feature for all classes. We focus on class-specific feature weighting approaches, which discriminatively assign each feature a specific weight for each class. This paper uses a statistical feature weighting technique and proposes a new class-specific deep feature weighting method for Multinomial Naïve Bayes text classifiers. In this deep feature weighting method, feature weights are not only incorporated into the classification formulas but they are also incorporated into the conditional probability estimates of Multinomial Naïve Bayes text classifiers. Experimental results for a large number of text classification datasets validate the effectiveness and efficiency of our method. INDEX TERMS Multinomial Naïve Bayes text classifiers, class-specific feature weighting, statistic, deep feature weighting. CHAOQUN LI received the Ph.D. degree from the

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“…Machine learning methods use several learning algorithms to determine the sentiment by training on a known dataset. Many of them rely on very basic classifiers, e.g., Naïve Bayes [24] or Support Vector Machines [31]. They are trained on a particular dataset using features such as bag of words or bigrams, and with or without part-of-speech (PoS) tags.…”

Section: B Sentiment Analysis and Natural Language Processingmentioning

confidence: 99%

Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis

Martínez-Castaño,

Pichel,

Gamallo

2018

Preprint

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In this paper we propose a new parallel architecture based on Big Data technologies for real-time sentiment analysis on microblogging posts. Polypus is a modular framework that provides the following functionalities: ( 1) massive text extraction from Twitter, (2) distributed non-relational storage optimized for time range queries, (3) memory-based intermodule buffering, (4) real-time sentiment classification, (5) near real-time keyword sentiment aggregation in time series, (6) a HTTP API to interact with the Polypus cluster and ( 7) a web interface to analyze results visually. The whole architecture is self-deployable and based on Docker containers.

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Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features

Cited by 9 publications

References 11 publications

GRASS: A Syntactic Text Simplification System based on Semantic Representations

GRASS: A Syntactic Text Simplification System based on Semantic Representations

Class-Specific Deep Feature Weighting for Naïve Bayes Text Classifiers

Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis

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