Opinion Summarization Fitur Produk Elektronik Pada Amazon.com Dengan Metode Maximum Entropy

ABSTRAKSI: Jumlah pelanggan toko online meningkat pesat seiring menjamurnya e-commerce dan meningkatnya jumlah para pedagang online. Para pelanggan dapat mereview produk secara online. Review pelanggan ini menjadi suatu sumber informasi yang sangat berguna baik bagi pelanggan maupun produk manufaktur. Pelanggan dapat menggunakan informasi tersebut untuk mendukung keputusan mereka dalam membeli suatu barang. Bagi produk manufaktur, mengerti pendapat pelanggan merupakan informasi yang berharga untuk perkembangan suatu produk, pemasaran, dan juga CRM (Customer Relationship Management). Tetapi dengan semakin banyaknya review suatu produk, memunculkan masalah yaitu menyulitkan pelangggan maupun produk manufaktur dalam mengevaluasi review yang ada.Jumlah pelanggan toko online meningkat pesat seiring menjamurnya e-commerce dan meningkatnya jumlah para pedagang online. Para pelanggan dapat mereview produk secara online. Review pelanggan ini menjadi suatu sumber informasi yang sangat berguna baik bagi pelanggan maupun produk manufaktur. Pelanggan dapat menggunakan informasi tersebut untuk mendukung keputusan mereka dalam membeli suatu barang. Bagi produk manufaktur, mengerti pendapat pelanggan merupakan informasi yang berharga untuk perkembangan suatu produk, pemasaran, dan juga CRM (Customer Relationship Management). Tetapi dengan semakin banyaknya review suatu produk, memunculkan masalah yaitu menyulitkan pelangggan maupun produk manufaktur dalam mengevaluasi review yang ada.Berdasarkan hasil pengujian didapatkan bahwa menggunakan metode klasifikasi maximum entropy menghasilkan performansi yang lebih baik daripada tanpa menggunakan maximume entropy.Kata Kunci : Data Mining, Opinion Mining, Opinion Summarization, Pos Tagging, Maximum EntropyABSTRACT: The number of customers increases significantly as the online shop e-commerce proliferation and the increasing number of online merchants. The costumers can review products online. Review from costumers is a source of information that is very useful for both the consumer and manufacturing products. The costumers can use the information to support their decision in purchasing an item. For manufactured products, understanding the customer\u27s opinion is valuable information for the development of a product, marketing, and also CRM (Customer Relationship Management). But with the increasing number of reviews of a product, raise issues which complicate the costumers and manufacturing products in evaluating the existing review.This thesis aims to summarize the existing reviews by grouping based on features and orientation of the opinion. Each review will be looked for the features that are discussed and defined the orientation of the opinion. There are three stages: (1) extracting the features of a product and identifying opinion related to the featured products in each sentence (feature extraction); (2) Identifying orientation of the opinion (sentiment analysis); (3) Generating summarize based on feature and orientation.Maximum entropy classification method used to classify the extracted features. Based on the test result found that using maximum entropy classification methods produce better performance than without using maximum entropyKeyword: Data Mining, Opinion Mining, Opinion Summarization, Pos Tagging, Maximum Entrop

Opinion Mining on Non-English Short Text

As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining

OBOME - Ontology based opinion mining in UBIPOL

Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated

Sentiment Analysis using an ensemble of Feature Selection Algorithms

To determine the opinion of any person experiencing any services or buying any product, the usage of Sentiment Analysis, a continuous research in the field of text mining, is a common practice. It is a process of using computation to identify and categorize opinions expressed in a piece of text. Individuals post their opinion via reviews, tweets, comments or discussions which is our unstructured information. Sentiment analysis gives a general conclusion of audits which benefit clients, individuals or organizations for decision making. The primary point of this paper is to perform an ensemble approach on feature reduction methods identified with natural language processing and performing the analysis based on the results. An ensemble approach is a process of combining two or more methodologies. The feature reduction methods used are Principal Component Analysis (PCA) for feature extraction and Pearson Chi squared statistical test for feature selection. The fundamental commitment of this paper is to experiment whether combined use of cautious feature determination and existing classification methodologies can yield better accuracy

Replication issues in syntax-based aspect extraction for opinion mining

Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.Comment: Accepted in the EACL 2017 SR