Analyzing User Behavior of Social Media

In recent time, analysis the user behavior from social media sites and applications plays a important role in current scenario. The users are people who are using social media sites and applications. To analyzing the behavior researcher deals with collecting data by opinions, identifying them. Classifying data according to the orientation of opinions and presenting their behavior from data. Analysis of behavior is an interesting job but it is also a very challenging procedure. Most of social media provides huge opinions on a particular product. The response/ opinions by user play a significant role in deciding the product�s future. These responses create positive or negative impact which plays a vital role in the field of e-commerce. It provides the useful result about product which have demands in the market or not, which is very useful for both customers as well as supplier of that product. In this paper researcher purposed some steps to analyze and express their views how social media data analyze market behavior for both company and customer

APLIKASI SENTIMENT MONITORING UNTUK TWITTER DENGAN ALGORITMA NAIVE-BAYES CLASSIFIER

Every day there are millions of opinion spread across social networks. This is often utilized by various parties to determine the opinion and sentiment of the public towards the product, brand or figures that they hold. Given the abundance of data and opinions, it is not possible to do sentiment analysis manually. In this research, author performs design and implementation of sentiment monitoring application, that could monitor people’s sentiment about a particular keyword, so it is known how the people response to those keywords, whether positive, negative or neutral. From various existing social networks, Twitter is chosen as the source of data that will be monitored. Classification algorithm used here is Naive-Bayes Classifier with Boolean Multinomial model, and feature extraction using unigram word. The training data used is 400,000 data for each type of sentiment, so the total is 1.200.000 data. In the process of classification and training, application will  perform  stemming  to  take  the  root  words  contained  within  the  tweet. Stemming algorithm used here is Confix Stripping. The  methodology  of  application  development  that  used  here is  staged delivery. Implementation of application is done using PHP programming language. The result of this research is a sentiment monitoring application that can monitor public sentiment about a particular keyword in a particular time frame. From testing using k-fold cross validation, obtained accuracy rate for sentiment classification amounted to 85%

Analisis Sentimen Publik Pada Media Sosial Twitter Terhadap Pelaksanaan Pilkada Serentak Menggunakan Algoritma Support Vector Machine

Pilkada Serentak is a very important event for the future viability regions and countries. Through this election people can cast their vote and elect representatives of the people according to their choice. Public respond can be expressed through twitter social media. Using twitter social media sentiment analysis can then be made about the public response to the implementation of the election simultaneously. The classification process can be detected via text tweeted by twitter users. In this study, the classification of responses detected by text because it is easily obtained and applied. This study determined the classification of the response to the Indonesian language text and increase accuracy by using SVM.Tweet classification method used by the categorical approach is divided into two classes tweet basic level: positive and negative. Data collected from Indonesian twitter tweet as much as 3000. The labeling is not done manually but using clustering method that divides the 3000 data into two groups. Cluster 1 as a group of positive tweets and Cluster 2 as a negative group tweet.2700 for training data and 300 for the test data. The stage of pre-processing the data includetokenization, casenormalization, stop word detection, and stemming. The process of classification using Support Vector Machine (SVM). Accuracy of SVM showed the highest yield that is 91% compared to the k-means clustering with the results of 82%

Analisis Sentimen Twitter Debat Calon Presiden Indonesia Menggunakan Metode Fined-Grained Sentiment Analysis

Media sosial, Twitter, saat ini telah banyak memberikan dampak besar dalam membangun opini, pandangan, sentimen, dan preferensi politik publik (menjelang Pemilihan Umum) berlangsung. Penelitian ini dilakukan untuk mengetahui percakapan di Twitter pada debat pertama calon presiden Republik Indonesia melalui hashtag dari kedua pasang calon. Selain itu, juga untuk mengetahui tentang kecenderungan masyarakat di Twitter terkait dengan debat yang sedang berlangsung tersebut cenderung positif, negatif, atau netral. Data percakapan di Twitter didapatkan melalui Twitter API yang diambil dengan bahasa Pemrograman R. Proses analisis sentimen ini menggunakan metode Fined-grained Sentiment Analysis yaitu, Jika satu tweet berisi lebih banyak kalimat positif daripada negatif, maka hasil keseluruhan akan positif dan bernilai (+1). Jika jumlah kalimat negatif lebih besar dari kalimat positif, maka hasil keseluruhan negatif dan bernilai (-1). Jika ada jumlah yang sama dari kalimat positif dan negatif dalam paragraf, maka hasilnya adalah netral dan bernilai (0). Hasil dari penelitian ini menunjukkan bahwa tweet sentimen dari kedua hashtag cenderung positif, lebih banyak daripada sentimen negatif dan netral

Knowledge management and social media: A scientometrics survey

The purpose of this research is to study the role of the social media for knowledge sharing. The study presents a comprehensive review of the researches associated with the effect of knowledge management in social media. The study uses Scopus database as a primary search engine and covers 1858 of highly cited articles over the period 1994-2019. The records are statistically analyzed and categorized in terms of various criteria using an open source software package named R. The findings show that researches have grown exponentially during the recent years and the trend has continued at relatively stable rates. Based on the survey, knowledge management is the keyword which has carried the highest citations followed by social media and social networking. Among the most cited articles, papers published by researchers in United States have received the highest citations, followed by United Kingdom and China

Comparación de métodos para clasificar comentarios de lugares turísticos por medio de análisis de sentimiento

Hoy en día los turistas luego de visitar algún destino plasman sus experiencias como opiniones en diversas fuentes turísticas, redes sociales y/o sitios web turísticos, siendo esta información valiosa para empresas turísticas o relacionadas a ello, para identificar en qué lugar se puede enriquecer la experiencia de la visita (una oportunidad de mejora). Asimismo, promover la atención de los turistas durante la planificación de sus viajes, ya que la información existente puede ser abrumadora. En esta investigación se tomó el sitio web TripAdvisor para adquirir los comentarios acerca los sitios de interés y se realizó la comparación de tres técnicas para la clasificación de estos comentarios: Support Vector Machine (SVM), Naïve Bayes (NB) y Método propuesto basado en SVM y Chi Square como método de selección de características. La técnica híbrida propuesta obtuvo el mejor resultado, seguido de SVM y por último Naïve Bayes cada una con 80.27%, 78.53% y 76.91% de precisión respectivamente. Se concluye que es factible realizar la clasificación automática y obtener los lugares con mayor proporción de reseñas negativas

Social media and e-commerce: A scientometrics analysis

he purpose of this research is to investigate the status and the evolution of the scientific studies on the effect of social networks on e-commerce. The study seeks to address the status of a set of scientific productions of researchers in the world indexed in Scopus based on scientometrics indicators. In total, 1926 articles were found and the collected data were analyzed using quantitative and qualitative indicators of scientometrics with bibliometrix R software package. The findings show that researches have grown exponentially since 2009 and the trend has continued at relatively stable rates. Thematic analysis shows that the subject had a significant but not well-developed research field. There is a high rate of cooperation with a rich research network among institutions in United States, European and Asian countries. Studies also show that research interest in this area is prevalent in developed countries. In addition, the lack of funds and complex analytical tools may be due to lack of studies in developing countries, especially in Africa. The study of the global trend of research through scientometrics helps managers and researchers in identifying countries and institutions with the greatest potential for scientific production, which allows them to develop their professions