SocialModeler is an easy to use application built using the Java programming language, which you can use to analyze news articles and blogs, in order to determine trends in article content. Using topic modeling methods and natural language processing techniques, SocialModeler outputs topic information and usage statistics.
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I am using Eclipse for your Java programming tutorial. I am totally new to Java programming, I am learning Java 8 by your tutorial. I really appreciate your help. Thank You! A: Here is the working code. There are two files, viz. i guess it is the main and second Java file. The main file contains the sample code, and the second file contains the actual code you’ve implemented. Main.java import java.io.*; public class Main { public static void main(String[] args) throws IOException { DataStreamReader dataStreamReader = new DataStreamReader(new BufferedReader(new FileReader(„file:///C:/Users/prashant/Downloads/21.txt”))); String[] lines = dataStreamReader.read(); System.out.println(„The file contains:” + lines.length + „lines”); Model model = new Model(lines); System.out.println(„Model statistics:”); model.printModelStatistic(); model.runTopicModel(5, 0.5); System.out.println(„Topics extracted:”); model.printExtractedTopic(); model.printUsageStats(10); } } DataStreamReader.java import java.io.BufferedReader; import java.io.FileNotFoundException; import java.io.FileReader; import java.io.IOException; import java.io.PrintStream; import java.nio.charset.StandardCharsets; public class DataStreamReader { // Wrapper class for reading the data from a file public class TextLine { private String line; public TextLine(String line) { this.line = line; } public String getLine() {
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——————————— * Used to analyse News articles and blogs with the aim of extracting information about the content * You can analyse the news articles or blogs based on your own criteria * Each article or blog is assigned a topic * Topics are derived by analyzing the words and use of words in the articles or blogs To access tutorials, installation and usage please visit: Download the appropriate installer package from: To use the Internet version of SocialModeler, simply copy the Java class files into your local installation directory * A Java Runtime Environment (JRE) version 7 or higher is required * Available under the following three options * 1. Run the SocialModeler.jar file directly * 2. Install the SocialModeler application and run directly from the start menu * 3. Install the SocialModeler application and run from a shortcut placed in your start menu * The software includes instructions in the instructions.txt file For support please contact: * * socialmoder@gmail.com 6.1 Overview ———— The SocialModeler package includes 4 java java class files. * SocialModeler.java – Main class * TopicModeler.java – General news content-analytics task * TopicLink.java – Use of topics (links) in the news content * TopicModeler.Parser.java – Parsing input for the topicModeler class The main SocialModeler java class provides an internal data model using which the workflow of the class is achieved. The model consists of sets of Inputs, Outputs, Topics, and RelatedTopics. There are four methods in the SocialModeler java class: * init() * finalize() * run() * parser() 6.1.1 The initial method ———————– The method init() initialises an input. This is the main method that’s called and the method calls the other initialization methods of the class. The Input class provides an interface for the use of the Class. These methods provide the init() method with a single input, by default the model 2f7fe94e24
SocialModeler
The core part of SocialModeler is a collection of NLP routines for extracting information from text documents. SocialModeler can analyze news articles, blogs, and other web content to identify the leading topics and keywords. The SocialModeler application is expected to be useful in a wide range of fields, because it is easy to use and gives a broad range of information. SocialModeler source code is also available for users to extend or modify this application, as well as for researchers who want to conduct their own studies. For example, a user can use SocialModeler to analyze an article related to the election of the Japanese Prime Minister on a daily, weekly, and monthly basis. Then, the user can process the information and use it to compare outcomes, in order to evaluate whether the news media is politically biased. By analyzing the most frequently used words in the article, the user can make inferences about the voting intention of the public. Furthermore, a user can extend the analysis by including a search engine process, and then obtain a list of the most frequently used articles in a given time interval. Using information of the users’ interests, the user can show the articles that a user is most likely to want to see, and then increase the visibility of that user’s opinion. SocialModeler Screenshot Twitter Feed Feature Comparison with Oloron et al. SocialModeler Features: SocialModeler is the product of an ongoing collaboration between the researchers of the Center for Neural Computation at the Massachusetts Institute of Technology. Our research deals with natural language processing (analysis of text data), topic modeling (analysis of text data), and information retrieval . Furthermore, SocialModeler is also an adaptation of the research and software developed by the Collaborative Research Centre (CRC/IITD-CSNET) at IIT Delhi. Our research group at the Massachusetts Institute of Technology has developed a number of software tools for social networking and information retrieval. These tools include SocialNetworkManager (SNA-1.0), FriendManager (FIMA-0.5), SocialBookmarks (SBO-0.1), MeetupManager (MIM-1.0), and MeetupDB (MID-1.0), and the research literature
What’s New in the SocialModeler?
SocialModeler allows you to easily, and automatically, classify and rank any text-based document based on the contents of the document and its context. This is achieved using natural language processing techniques combined with the use of the Latent Semantic Analysis (LSA) algorithm. Features • Automatic classification and ranking of documents• 100% free How it works • Read the article, or blog post, or web page, or any text-based document. When a document is read, the document text is automatically scanned by SocialModeler, using a version of the LSA algorithm known as latent semantic analysis. • Using a set of pre-defined semantic templates and the text of the document, SocialModeler identifies and ranks the most important information within the document. • From these ranked information, we can identify a number of useful information about the topic that the document covers. • SocialModeler outputs a detailed report for each document and each of its key passages, which can be viewed and downloaded using a popular Web browser. • We can also use SocialModeler to classify existing documents based on the number of times that they are read. This helps with document management. Example use • Query a document for a list of keyword matches.• Query a document for a list of specific topics.• Create a new document, using keyword-based templates.• After creating a document, query it to identify all related topics.• Create a document and query it.• After creating a document, query it for one or more specific topics. Common use • Create a document that covers a specific topic and query it for new information.• Report on the number of new documents that were created based on the given topic.• Rerank a document’s rank over time. In depth Latent Semantic Analysis Latent Semantic Analysis (LSA) is a powerful approach to analyzing text, and is used by SocialModeler to identify a number of different types of semantic information. As an example, given two phrases, the first “Movie” and the second “Paramount Pictures”, we may know that the second phrase contains specific information about the first phrase. To illustrate this, you might think that the two phrases can be related because a movie is a type of film. However, if a film is made by Paramount Pictures, it would be considered that these two phrases are related, and, as a
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