A hybrid cuckoo search method (CSK) has been presented for Twitter sentiment analysis.CSK modifies the random initialization of population in cuckoo search (CS) by K-means to resolve the problem of random initialization.The proposed algorithm has outperformed five popular algorithms.The statistical analysis has been done to validate the performance of the proposed algorithm. Sentiment analysis is one of the prominent fields of data mining that deals with the identification and analysis of sentimental contents generally available at social media. Twitter is one of such social medias used by many users about some topics in the form of tweets. These tweets can be analyzed to find the viewpoints and sentiments of the users by using clustering-based methods. However, due to the subjective nature of the Twitter datasets, metaheuristic-based clustering methods outperforms the traditional methods for sentiment analysis. Therefore, this paper proposes a novel metaheuristic method (CSK) which is based on K-means and cuckoo search. The proposed method has been used to find the optimum cluster-heads from the sentimental contents of Twitter dataset. The efficacy of proposed method has been tested on different Twitter datasets and compared with particle swarm optimization, differential evolution, cuckoo search, improved cuckoo search, gauss-based cuckoo search, and two n-grams methods. Experimental results and statistical analysis validate that the proposed method outperforms the existing methods. The proposed method has theoretical implications for the future research to analyze the data generated through social networks/medias. This method has also very generalized practical implications for designing a system that can provide conclusive reviews on any social issues.

Twitter sentiment analysis using hybrid cuckoo search method

Cluster analysis is a descriptive task that seek to identify homogenous group of object and it is also one of the main analytical method in data mining. K-mean is the most popular partitional clustering method. In this paper we discuss standard k-mean algorithm and analyze the shortcoming of k- mean algorithm. In this paper three dissimilar modified k-mean algorithm are discussed which remove the limitation of k-mean algorithm and improve the speed and efficiency of k-mean algorithm. First algorithm remove the requirement of specifying the value of k in advance practically which is very difficult. This algorithm result in optimal number of cluster Second algorithm reduce computational complexity and remove dead unit problem. It select the most populated area as cluster center. Third algorithm use simple data structure that can be used to store information in each iteration and that information can be used in next iteration. It increase the speed of clustering and reduce time complexity.

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A Review of K-mean Algorithm

In this article, efficient power saving scheme and corresponding algorithm must be developed and designed in order to provide reasonable energy consumption and to improve the network lifetime for wireless sensor network systems. The cluster-based technique is one of the approaches to reduce energy consumption in wireless sensor networks. In this article, we propose a saving energy clustering algorithm to provide efficient energy consumption in such networks. The main idea of this article is to reduce data transmission distance of sensor nodes in wireless sensor networks by using the uniform cluster concepts. In order to make an ideal distribution for sensor node clusters, we calculate the average distance between the sensor nodes and take into account the residual energy for selecting the appropriate cluster head nodes. The lifetime of wireless sensor networks is extended by using the uniform cluster location and balancing the network loading among the clusters. Simulation results indicate the superior performance of our proposed algorithm to strike the appropriate performance in the energy consumption and network lifetime for the wireless sensor networks.

/pdf/an-efficient-cluster-based-power-saving-scheme-for-wireless-32okjdtdan.pdf

An efficient cluster-based power saving scheme for wireless sensor networks

A review of feature selection techniques in sentiment analysis

Cluster analysis has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences, in order to identify natural groups in large amounts of data. Clustering has also been widely adopted by researchers within computer science and especially the database community. K-means is the most famous clustering algorithms. In this paper, the performance of basic k means algorithm is evaluated using various distance metrics for iris dataset, wine dataset, vowel dataset, ionosphere dataset and crude oil dataset by varying no of clusters. From the result analysis we can conclude that the performance of k means algorithm is based on the distance metrics for selected database. Thus, this work will help to select suitable distance metric for particular application. General Terms Clustering algorithms, Pattern recognition.

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Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics

That traditional K-mean algorithm is a widely used clustering algorithm, with a wide application. In light of the disadvantage of K-mean algorithm, improvement is made to the traditional K-mean algorithm, a k value learning algorithm is proposed. Using genetic algorithm to optimize the K value, and improve clustering performance.

An improved K-means clustering algorithm

Sentiment classification has attracted increasing interest from Natural Language Processing. This paper explores the genetic algorithm to extract the best feature collections from the semantic features of emotional collections. Conditional Random Fields (CRFs) is employed to model the emotional tendency of web pages which are divided into different types of comments, such as positive comments, negative comments and objective comments. Experimental results on both the product reviews and the 1998 People's Daily corpus show that the proposed algorithm works reasonable in the real calculation.

Sentiment classification using genetic algorithm and Conditional Random Fields

RSS (most commonly expanded as "Really Simple Syndication") is a family of web feed formats used to publish frequently updated works-such as blog entries, news headlines, audio, and video-in a standardized format. For some in order to release the contents of the RSS format, a search using general search engine is inefficient, slow updates, while the RSS search engines to overcome these shortcomings to achieve high efficiency, high-speed searching of this page is published through the RSS seeds. This paper describes the concept of RSS and RSS search engine, the concept, RSS search engine, personalized search in e-commerce applications.

Jian Zhu

Papers

An improved K-means clustering algorithm

Sentiment classification using genetic algorithm and Conditional Random Fields

Application of e-commerce personality searching based on RSS