In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.

Text Classification Algorithms: A Survey

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, which Latent Dirichlet allocation (LDA) is one of the most popular methods in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper can be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated scholarly articles highly (between 2003 to 2016) related to Topic Modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey

The use of renewable energy resources, such as solar, wind, and biomass will not diminish their availability. Sunlight being a constant source of energy is used to meet the ever-increasing energy need. This review discusses the world's energy needs, renewable energy technologies for domestic use, and highlights public opinions on renewable energy. A systematic review of the literature was conducted from 2009 to 2018. During this process, more than 300 articles were classified and 42 papers were filtered for critical review. The literature analysis showed that despite serious efforts at all levels to reduce reliance on fossil fuels by promoting renewable energy as its alternative, fossil fuels continue to contribute 73.5% to the worldwide electricity production in 2017. Conversely, renewable sources contributed only 26.5%. Furthermore, this study highlights that the lack of public awareness is a major barrier to the acceptance of renewable energy technologies. The results of this study show that worldwide energy crises can be managed by integrating renewable energy sources in the power generation. Moreover, in order to facilitate the development of renewable energy technologies, this systematic review has highlighted the importance of public opinion and performed a real-time analysis of public tweets. This example of tweet analysis is a relatively novel initiative in a review study that will seek to direct the attention of future researchers and policymakers toward public opinion and recommend the implications to both academia and industries.

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Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions

The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, ie, emotion indication and emotion correlation We further incorporate the signals into an unsupervised learning framework for sentiment analysis In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals

Unsupervised sentiment analysis with emotional signals

A survey of multimodal sentiment analysis

A huge amount of online information, rich web resources are highly unstructured and such natural language are not solvable by machine directly. The increased demand to capture opinions of general public about social events, campaigns and sales of the product has led to study of the field opinion mining and sentiment analysis. Opinion refers to extraction of lines in raw data which expresses an opinion. Sentiment analysis identifies polarity of extracted opinions. The major challenge lies in analyzing the sentiments and identifying emotions expressed in texts. This paper presents a survey which covers a problem of sentiment analysis, techniques and methods used for the same.

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A Survey of Opinion Mining and Sentiment Analysis

With the increasing demands on 3D applications and the easy capturing of 2D images nowadays, building 3D models from 2D images receives much attention in the past few years. 3D modeling is widely used in several fields3D graphics in computer games, software architecture models and 3D printing. 3D models represent a physical body using a collection of points in 3D space, connected by various geometric entities such as triangles, lines, curved surfaces, etc. 3D modeling is the process of developing a mathematical representation of any three-dimensional surface of an object. Today, 3D models are used in a wide variety of fields. The engineering community uses them as designs of new devices, vehicles and structures as well as a host of other uses. A variety of machine learning algorithms are being studied and implemented to find or estimate the depth information which is unavailable in conventional 2D image. We apply Computer Vision algorithm considering aspect of binocular disparity where we use 2 images of same scene captured from different viewpoints. Then we obtain depth of the object and further construct depth map. After mapping the points from depth maps of various images captured we apply correct texturing to obtain full 3D model of the object.capturing one eye‟s view, and depth information is computed using binocular disparity. Here we focus only on binocular and multi-ocular images as input. The two or more input images could be taken either by multiple fixed cameras located at different viewing angles or by a single camera with moving objects in the scenes. A three-dimensional (3D) visualization enables consumers to interact with products and creates a sense of being in a simulated real world. As the consumer gets a real view of the products they tend to get attracted towards the product thus increasing the sale. It gives us an edge over other competitors as 3D visualization is different and it stands out from others. It also makes shopping more convenient and easy for the customers. In this paper we focus only on binocular and multiocular images as input. We study computer vision algorithm, binocular disparity, silhouette and visual hull. In computer vision algorithm, SURF and ORB features descriptors are used to extract information from images. Binocular disparity uses 2 images of the same scene from different viewpoints. In silhouette the object is separated from the background and silhouette cones are formed. Intersection of silhouette cones is called visual hulls. General Terms 2D images, Computer Vision, 3D reconstruction

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Gayatri Prabhu

Papers

A Survey of Opinion Mining and Sentiment Analysis

3D Visualization of Products for Online Shopping