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

An Efficient CBIR Technique with YUV Color Space and Texture Features

In areas of government, academia and hospitals, large collections of digital images are being created. These image collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is presented in this paper, which used YUV color space and wavelet transform approach for feature extraction. Firstly, the color space is quantified in non-equal intervals, then constructed one dimension feature vector and represented the color feature. Similarly, the texture feature extraction is obtained by using wavelet. Finally, color feature and texture feature are combined based on wavelet transform. The image retrieval experiments specified that visual features were sensitive for different type images. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that YUV texture feature based on wavelet transform has better effective performance and stability than the RGB and HSV. The same work is performed for the RGB and HSV color space and their results are compared with the proposed system. The result shows that CBIR with the YUV color space retrieves image with more accuracy and reduced retrieval time. Keywords---Content based image retrieval, Wavelet transforms, YUV, HSV, RG

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data

Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

A generic news story segmentation system and its evaluation

The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters

A decision forest based feature selection framework for action recognition from RGB-Depth cameras

In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). On MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset, collected by our group (10 physical exercise classes), the accuracy is 98%. The approach can also be used to provide insights on the spatiotemporal dynamics of human actions

Novel convolution-based signal processing techniques for an artificial olfactory mucosa

As our understanding of the human olfactory system has grown, so has our ability to design artificial devices that mimic its functionality, so called electronic noses (e-noses). This has led to the development of a more sophisticated biomimetic system known as an artificial olfactory mucosa (e-mucosa) that comprises a large distributed sensor array and artificial mucous layer. In order to exploit fully this new architecture, new approaches are required to analyzing the rich data sets that it generates. In this paper, we propose a novel convolution based approach to processing signals from the e-mucosa. Computer simulations are performed to investigate the robustness of this approach when subjected to different real-world problems, such as sensor drift and noise. Our results demonstrate a promising ability to classify odors from poor sensor signals

Detecting Family Resemblance: Automated Genre Classification.

This paper presents results in automated genre classification of digital documents in PDF format. It describes genre classification as an important ingredient in contextualising scientific data and in retrieving targetted material for improving research. The current paper compares the role of visual layout, stylistic features and language model features in clustering documents and presents results in retrieving five selected genres (Scientific Article, Thesis, Periodicals, Business Report, and Form) from a pool of materials populated with documents of the nineteen most popular genres found in our experimental data set.