Using Bio-inspired intelligence for Web opinion Mining

Stylios, George; Katsis, Christos D.; Christodoulakis, Dimitris

doi:10.5120/15207-3610

Cited by 14 publications

(8 citation statements)

References 40 publications

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“…In 2014 another hybrid approach of PSO and ACO (Ant Colony Optimization) was proposed for web-based opinion mining by George Stylios. This bio-inspired method achieved 90.59% accuracy using 10 fold cross validation approach while outperforming the C4.5 algorithm with 83.66% accuracy [1]. In 2014 PSO was also used to select features for thermal face recognition and results showed a success rate of 90.28% for all images [22].…”

Section: Literature Reviewmentioning

confidence: 98%

“…Sentiment classification is basically a task of opinion mining used to extract people's unanimous reviews about any topic, event, or product [1]. Sentiment analysis or opinion mining is a step-wise procedure to produce the results of this classification task.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mostly these reviews are in the form of text. Publicly shared reviews over blogs or articles are used to identify the unanimous customer opinion for any product or service to help maintain good consumerism [1]. The major issue that arises while gathering the data from social networking sites is that the reviews mostly contain grammatical and/or spelling mistakes and data is usually so large that correcting those mistakes is humanly impossible.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

Naz

Zafar

Khan

2019

Data

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Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Naïve Bayes (NB). For the classification of sentiments, 15–20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer’s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.

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Section: Literature Reviewmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

Naz

Zafar

Khan

2019

Data

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“…The individual format they proposed is in binary format, in which each bit shows the presence of a word. In [53], PSO/ACO2, a hybrid of Particle Swarm Optimization and Ant Colony Optimization is used for finding whether a post contains specific arguments. In [54], the Arora et al use genetic programming for constructing new evolved features based on existing features to improve accuracy.…”

Section: Application Of Evolutionary Computation In Sentiment Analysismentioning

confidence: 99%

ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs

Keshavarz

Abadeh

2017

Knowledge-Based Systems

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Sentiment analysis is about classifying opinions expressed in text. The aim of this study is to improve polarity classification of sentiments in microblogs by building adaptive sentiment lexicons. In the proposed method, corpora-based and lexicon-based approaches are combined and lexicons are generated from text. The sentiment classification is formulated as an optimization problem, in which the goal is to find optimum sentiment lexicons. A novel genetic algorithm is then proposed to solve this optimization problem and find lexicons to classify text. The algorithm generates adaptive sentiment lexicons, and then a meta-level feature is extracted based on it, which is then used alongside Bing Liu's lexicon and n-gram features. The experiments are conducted on six datasets. In terms of accuracy, the results outperform the state-of-the-art methods proposed in the literature in two of the datasets. Also, in four of the datasets, the proposed approach outperforms in terms of Fmeasure. Applying the proposed method on six datasets, the accuracy is higher than 80% in all six datasets and the F-measure is higher than 80% in four of these datasets. Using the sentiment lexicons created by the proposed algorithm, one can get a better understanding of the specific language and culture of Twitter users and sentiment orientation of words in different contexts. It is

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“…Biologically inspired AI techniques have also proved to be relevant for solving this problem, as many works in the last couple of years use these approaches for opinion mining, such as it is the case of genetic algorithms [14], particle swarm and ant colony optimization [39], neural networks [2] or a combination of several of them [17].…”

Section: Related Workmentioning

confidence: 99%

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

Gómez¹,

Mingueza²,

Pozo³

2015

IJIMAI

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-This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones.

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Using Bio-inspired intelligence for Web opinion Mining

Cited by 14 publications

References 40 publications

Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

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