Metaheuristic algorithms for feature selection in sentiment analysis

Ahmad, Siti Rohaidah; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

doi:10.1109/sai.2015.7237148

Cited by 55 publications

(19 citation statements)

References 33 publications

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“…The increasing of b value improves recognition effect to some extent. In view of the recognition conditions for two corpuses, b value is with better effect while increasing in the interval of [5,30], but the improvement of performance is not significant if continue to increase b value, which explains that b=30 is a turning point for identifying the stability of performance. Moreover, generation number L of subspace has greater influence on recognizing the performance of model.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…(2) For the selection of sentiment characteristics, firstly, it adopts word segmentation tool ICTCLAS2012 [5] of Chinese Academy of Sciences to make batch word segmentation for text set, saves word segmentation results and then extracts sentiment words included in word segmentation results as characteristics based on sentiment dictionary made based on scheme (1). ( based on linear kernel function for its extensive application in text classification and good classification performance and processing speed in sparse feature space.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The experimental data used in this paper is from Chinese comment corpus [5] about hotel and notebook provided by Tan Songbo. This corpus set is balanced data set with 2000 positive and 2000 negative respectively.…”

Section: Data Setmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of massive unsupervised text sentiment based on rough set time series model

Du¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract:One text sentiment classifier constructed based on the mechanism of dynamic classification of sample space has been proposed to improve the accuracy of Chinese text sentiment recognition by starting from the perspective of integrated learning. This algorithm makes full use of the identification information within training sample space, makes adaptive classification for sample space by introducing kernel smoothing method, forms several multi-granularity subspaces with differences, and then constructs base classifier in each subspace respectively and finally integrates the output of all base classifiers to produce the final prediction results. Experimental results on Chinese data set have shown that this algorithm is superior to Bagging, Adaboost and other algorithms in precision ratio and recall ratio etc and is also with good application prospect in the sentiment recognition of large-scale sample set.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of massive unsupervised text sentiment based on rough set time series model

Du¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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“…The IMDb is a large database with related and comprehends information on movies-past, present and future [18]. It commenced as shell script set and data file.…”

Section: Internet Movie Database (Imdb) Datasetmentioning

confidence: 99%

Eco Inspired Bees: A Novel Feature Selection Mechanism for Sentiment Analysis

Sharmila¹,

Sivajothi²

2017

Int. J. of Pure and Appl. Math.

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Sentiment analysis also called as opinion mining is defined as the programmed learning of public's opinion, evaluations, and sentiment on different events, objects as well as their respective properties. Recently, it created high impact in academia and industry due to number of growing research problems and various applications. Selecting the features play a vital role in supreme data mining tasks since it assists in the reduction of data dimensionality by leaving the non-relevant features behind. Present work applies the ecological concept of habitat, ecological relationship and ecological succession for building an Ecology-Inspired Optimization algorithm named as Ecology Inspired Artificial Bee Colony algorithm (EI-ABC).Proposed method uses various population of candidate solution which cooperates and coevolves with one another, as per the given meta-heuristic algorithm. Sentiments are classified by using two classifiers viz., K Nearest Neighbor (KNN) and Classification and Regression Tree (CART). Result suggests that the EI-ABC algorithm proves to be interesting alternative for numerical optimization.

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“…Thither are numerous approaches (including machine learning approach, lexicon based approach) for calculating sentiment. [4] Prefers the metaheuristic algorithm which is used as feature selection in sentiment analysis. This algorithmis used for selecting the optimum features from the customer reviews.…”

mentioning

confidence: 99%