The era of E-commerce and availability of data in every field of operations in an enormous volume that implies to Big Data is one of the biggest sources of competitive advantage for the organizations in this digital world. It provides useful information to grow businesses by posting the advertisement and help consumers find the relevant product according to their preferences. The focus of this research is on the advertisement strategies analysis under which a Business employs several online advertisement strategies in order to appeal to the consumer. This research work will present a detailed analysis in user behavioral to use for business or Online Behavioral advertising and provide the framework of how Enterprise Resource Planning systems track the targeted audience and show their content. The paper's prime objective is to classify and effectively run targeted advertising using the data that shows user's retail behavior. This is where an Enterprise Resource Planning driven data will give rise to behavioral analytics. In addition to this, various data streaming technologies are also emphasized that will help to create a pipeline for the huge amount of data in Enterprise Resource Planning's database.

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A Novel Technique for Behavioral Analytics Using Ensemble Learning Algorithms in E-Commerce

Real-time sentiment analysis allows to monitor social networks and process negative comments before the situation worsens, gives an opportunity to gather customer response to the marketing campaigns or product launches and get an overview of how customers react to the product or prevent negative ones events determining the mood of people (posts on social networks, videos on YouTube, Twitch or live). The development of this system aims at testing the capabilities of the natural language processing system in the recognition of the Ukrainian language.

Ukrainian Language Chatbot for Sentiment Analysis and User Interests Recognition based on Data Mining

The semantic orientation (also referred to as prior polarity) of a word plays an important role in automatic sentence-level sentiment analysis. Several approaches have been proposed wherein a lexicon of words marked with their polarities is exploited to infer the meaning of sentences. However, relying on prior word polarity may produce inaccurate decisions. This is because we may find negative-sentence sentiments that include words with positive prior polarities or vice versa. In this article, we propose an approach to sentence-level sentiment analysis that exploits knowledge encoded in heavy-weight semantic graphs to assist in discovering the meaning of a word in the context of the sentence where it appears. In this context, we build contextual semantic networks for indexing sentences and expand them with semantically/lexically-relevant terms in an attempt to disambiguate the meanings of word mentions in sentences. In order to verify the effectiveness of the proposed approach, we have developed a prototype system using a real-world dataset that contains 46830 sentiment sentences along with a gold-standard that comprises 10000 movie reviews that are labelled under five sentiment categories (very negative, negative, neutral, positive, very positive). Findings indicate that enriching the semantic graphs of sentiment sentences with NOUN-based synonyms and hypernyms has improved the overall quality of baseline sentiment analysis techniques.

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Semantic Graph based Term Expansion for Sentence-Level Sentiment Analysis

Data mining is the procedure to uncover patterns, correlations, and anomalies (if any) within big datasets to foresee outcomes. In this chapter, a framework of the basic ideas and characteristics related to data mining in line with the theme of social networking is laid out. The details of data mining are explored in various directions. Thereafter, the concepts of modern social networking and its multiple features are provided. Furthermore, a brief discussion on clustering and classification, clustering algorithms, multi-view clustering is presented to elucidate the direction of data mining and social networking. Finally, various applications to data mining in the context of social networking are presented. 

An introduction to data mining in social networks

This article dwells upon research concerning human language processing techniques, namely emotion analysis, conducted to identify emotions of author quotations in English newspaper articles. The publication describes general information about human language processing, brief description of analogues based on machine learning and neural network to analyse emotions in text. The main goal is to develop intelligent system for sentiment analysis of English language quotations and keywords identification that influences public opinion. The article describes sequence of program actions and tools that have been used. Then, statistics of tasks performed by neural networks with various functions of activators have been described. In the publication, product demo version has been described, and primary usage scenario has been depicted. In the end, research results have been summarised.

Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool

Social networks have increased their demand extensively for mining texts. Opinions are used to express views and reviews are used to provide information about how a product is perceived. The reviews available online can be available in thousands, so making the right decision to select a product becomes a very tedious task. Several research works has been proposed in the past but they were limited to certain issues discussed in this paper. A dynamic system is proposed based on the features using ontology followed with classification. Classifying information from such text is highly challenging. We propose a novel method of extracting aspects using ontology and further categorizing these sentiments into positive, negative and neutral category using supervised leaning technique. Opinion Mining is a natural language processing task that mine information from various text forums and classify them on the basis of their polarity as positive, negative or neutral. In this paper, we demonstrate machine learning algorithms using WEKA tool and efficiency is evaluated using information retrieval search strategies.

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Opinion target extraction with sentiment analysis

Abstract: Malware is still the most dangerous issue facing internet users in today's online environment. The newly created malware is separate from the traditional kind, has a more dynamic design, and typically combines traits from two or more different malware types. comparing the various memory acquisition tools that are available, each of which has a varying performance dependent on the setups, installed hardware, and operating system version. If the ending character is not present. To address the growing malware issue, new methodologies like machine learning must be employed. Investigate how cybersecurity is used in this study for malware detection and machine learning. In this study will look at the PE (portable executable) headers of malware and non-malware samples in order to build a malware classifier that can identify if malware is there or not. The development of behavior-based malware detection and classification methods using various machine learning approaches is addressed in this study along with behavior-based detection methods itself

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Malware Analysis Using Memory Forensics

This paper explores the use of a light based measurement technique for the level measurement of cryogenic liquids. Differential pressure transmitters are commonly used to measure the levels of cryogenic liquids such as liquid nitrogen and liquid oxygen in cryogenic tanks. They require the liquid inside the temperature controlled tank to be drawn out into impulse lines. This causes a number of problems, one of the most common one is Chocking of the impulse lines, which ultimately results in a significant loss of material.

Pragyaditya Das

Papers

Opinion target extraction with sentiment analysis

Malware Analysis Using Memory Forensics

A Novel Cryogenic Liquid Level Sensing Technique: Harnessing the Optical Properties of a Cryogenic Liquid.