209 research outputs found
Ranking influencers of social networks by semantic kernels and sentiment information
No full textInspired by the importance of social media, a Social Network Opinion Leaders (SNOL) system has been proposed in this paper. The purpose of this system is to identify topic-based opinion leaders of social media. In order to accomplish this goal, several steps have been taken, such as data collection, data processing, data analysis, data classification, ranking of topic-based opinion leaders, and evaluation. The SNOL system has two main parts. In the first part, collected tweets are classified by semantic kernels for topic-based analysis. In the second part, leadership scores are given to each user in the network according to topic modeling and user modeling results. Leadership scores are then calculated with the formula generated and opinion leaders are determined for each category. Experiments are performed on data gathered from Twitter including 17,234,924 tweets from 38,727 users. The evaluation of opinion leader detection is a difficult job since there is no standard method for identifying opinion leaders. Therefore, the evaluation of the results of this study has been done using two different methods, retweet count and spread score, to prove that the suggested methodology outperforms the PageRank algorithm. The results have also been evaluated considering the user-topic sentiment correlation of the retrieved lists. Furthermore, SNOL has been compared against some opinion leader detection methods previously presented in the literature. The experimental results show that SNOL generates remarkably higher performance than the PageRank algorithm and other existing algorithms in the literature for nearly all topics and all selected top N opinion leaders
A corpus-based semantic kernel for text classification by using meaning values of terms
No full textText categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. Recently, there is an increased interest in the exploitation of background knowledge such as ontologies and corpus-based statistical knowledge in text categorization. It has been shown that, by replacing the standard kernel functions such as linear kernel with customized kernel functions which take advantage of this background knowledge, it is possible to increase the performance of SVM in the text classification domain. Based on this, we propose a novel semantic smoothing kernel for SVM. The suggested approach is based on a meaning measure, which calculates the meaningfulness of the terms in the context of classes. The documents vectors are smoothed based on these meaning values of the terms in the context of classes. Since we efficiently make use of the class information in the smoothing process, it can be considered a supervised smoothing kernel. The meaning measure is based on the Helmholtz principle from Gestalt theory and has previously been applied to several text mining applications such as document summarization and feature extraction. However, to the best of our knowledge, ours is the first study to use meaning measure in a supervised setting to build a semantic kernel for SVM. We evaluated the proposed approach by conducting a large number of experiments on well-known textual datasets and present results with respect to different experimental conditions. We compare our results with traditional kernels used in SVM such as linear kernel as well as with several corpus-based semantic kernels. Our results show that classification performance of the proposed approach outperforms other kernels. (C) 2015 Elsevier Ltd. All rights reserved
A novel semantic smoothing kernel for text classification with class-based weighting
No full textIn this study, we propose a novel methodology to build a semantic smoothing kernel to use with Support Vector Machines (SVM) for text classification. The suggested approach is based on two key concepts; class-based term weighting and changing the orthogonality of vector space. A class-based term weighting methodology is used for transformation of documents from the original space to the feature space. This class-based weighting basically groups terms based on their importance for each class and consequently smooths the representation of documents. This is accomplished by changing the orthogonality of the Vector Space Model (VSM) with introducing class-based dependencies between terms. As a result, on the extreme case, two documents can be seen as similar even if they do not share any terms but their terms are similarly weighted for a particular class. The resulting semantic kernel can directly make use of class information in extracting semantic information between terms, therefore it can be considered as a supervised kernel. For our experimental evaluation, we analyze the performance of the suggested kernel with a large number of experiments on benchmark textual datasets and present results with respect to varying experimental conditions. To the best of our knowledge, this is the first study to use class-based term weighting in order to build a supervised semantic kernel for SVM. We compare our results with kernels that are commonly used in SVM such as linear kernel, polynomial kernel, Radial Basis Function (RBF) kernel and with several corpus-based semantic kernels. According to our experimental results the proposed method favorably improves classification accuracy over linear kernel and several corpus-based semantic kernels in terms of both accuracy and speed. (C) 2015 Elsevier B.V. All rights reserved
A new hybrid semi-supervised algorithm for text classification with class-based semantics
No full textVector Space Models (VSM) are commonly used in language processing to represent certain aspects of natural language semantics. Semantics of VSM comes from the distributional hypothesis, which states that words that occur in similar contexts usually have similar meanings. In our previous work, we proposed novel semantic smoothing kernels based on classspecific transformations. These kernels use class term matrices, which can be considered as a new type of VSM. By using the class as the context, these methods can extract class specific semantics by making use of word distributions both in documents and in different classes. In this study, we adapt two of these semantic classification approaches to build a novel and high performance semi-supervised text classification algorithm. These approaches include Helmholtz principle based calculation of term meanings in the context of classes for initial classification and a supervised term weighting based semantic kernel with Support Vector Machines (SVM) for the final classification model. The approach used in the first phase is especially good at learning with very small datasets, while the approach in the second phase is specifically good at eliminating noise in a relatively large and noisy training sets when building a classification model. Overall, as a semantic semi-supervised learning algorithm, our approach can effectively utilize abundant source of unlabeled instances to improve the classification accuracy significantly especially when the amount of labeled instances are limited. (C) 2016 Elsevier B.V. All rights reserved
Semantic text classification: A survey of past and recent advances
No full textAutomatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms
Instance labeling in semi-supervised learning with meaning values of words
No full textIn supervised learning systems; only labeled samples are used for building a classifier that is then used to predict the class labels of the unlabeled samples. However, obtaining labeled data is very expensive, time consuming and difficult in real-life practical situations as labeling a data set requires the effort of a human expert. On the other side, unlabeled data are often plentiful which makes it relatively inexpensive and easier to obtain. Semi-Supervised Learning methods strive to utilize this plentiful source of unlabeled examples to increase the learning capacity of the classifier particularly when amount of labeled examples are restricted. Since SSL techniques usually reach higher accuracy and require less human effort, they attract a substantial amount of attention both in practical applications and theoretical research. A novel semi-supervised methodology is offered in this study. This algorithm utilizes a new method to predict the class labels of unlabeled examples in a corpus and incorporate them into the training set to build a better classifier. The approach presented here depends on a meaning calculation, which computes the words' meaning scores in the scope of classes. Meaning computation is constructed on the Helmholtz principle and utilized to various applications in the field of text mining like feature extraction, information retrieval and document summarization. Nevertheless, according to the literature, ILBOM is the first work which uses meaning calculation in a semi-supervised way to construct a semantic smoothing kernel for Support Vector Machines (SVM). Evaluation of the proposed methodology is done by performing various experiments on standard textual datasets. ILBOM's experimental results are compared with three baseline algorithms including SVM using linear kernel which is one of the most frequently used algorithms in text classification field. Experimental results show that labeling unlabeled instances based on meaning scores of words to augment the training set is valuable, and increases the classification accuracy on previously unseen test instances significantly
Identifying Topic-based Opinion Leaders in Social Networks by Content and User Information
No full textWord sense disambiguation using semantic kernels with class-based term values
No full textIn this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval(1)). With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better than others?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?, 4.) Is their combination superior to either type?, 5.) Is ensemble of these kernels perform better than the baseline?, 6.) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms can outperform baseline in terms of F-score
Turkish sentiment analysis: a comparative study on different sentiment dictionaries with generated features and presenting a new sentiment dictionary
No full textSentiment analysis is a research area that aims to find out people\"s opinions by matching data to topics, notions, etc. There are several approaches for sentiment analysis (e.g., machine learning-based, dictionary-based, hybrid-based, etc.). In this study, we presented a new tripolar Turkish sentiment dictionary, SentiMenTR, which consists of bigrams and unigrams. To compare the performances of SentiMenTR and other Turkish sentiment dictionaries (SWNetTR++ and SentiTurkNet), we conducted experiments on two Turkish datasets containing documents labeled as negative or positive. For experiments, firstly, we vectorized the documents by features extracted using polarity scores belonging to dictionaries. Afterward, we fitted machine learning models with these features. According to the experiment results, SentiMenTR performed better than other dictionaries. We aim to extend our dictionary, develop a negation handler module, and conduct more comprehensive experiments with deep learning methods in the future
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