Analyzing Product Usage Based on Twitter Users Based on Data mining Process

Analyzing Product Usage Based on Twitter Users Based on Datamining Process

Table of Contents





Abstract

This paper further enhances the techniques and chronological methods in order to perform the corresponding manipulation and further prediction analysis. We have acquired a real-time dataset based on the twitter user’s comments sections. The uniqueness of the dataset is that we have extracted only the particular comments which syntheses a particular word based on the product. Then further repetitive extraction is made in order to complete the dataset. Our dataset has three columns based on the ideology that the particular user or our focused subject has enhanced any detail about the product that we are observing. In this precise dataset, we have taken the subject about product usage by the users that have been manufactured by the companies Google and Apple. Both technology giants have well versed their technology reign in this era and their further focused on their upcoming cyber projects and their products will be more advanced in the future. As they are involved in further optimization in their devices and increasing their specifications. It would be a complex task to accomplish their project without the feedback, pros, and cons of their predecessor projects. They can extract such features from the twitter API dataset and they could further enhance their product. They could analyze the drawback, whether the product has reached the market and came out with success all type of information can be extracted from social media instead of conducting a survey. Such a process would be more hectic classification and we cannot predict any accurate results. So we proceed with the social media Data mining Process.

  • Author Keywords

    • dataset,
    • data mining,
    • API,
    • analyze
  • Controlled Indexing

    • application program interfaces,
    • data mining,
    • social networking (online)
  • Non-Controlled Indexing

    • chronological methods,
    • corresponding manipulation,
    • prediction analysis,
    • time dataset,
    • Twitter user,
    • particular comments,
    • particular word,
    • repetitive extraction,
    • focused subject,
    • precise dataset,
    • technology giants,
    • technology reign,
    • upcoming cyber projects,
    • predecessor projects,
    • Twitter API dataset,
    • product usage,
    • Twitter users,
    • social media data mining process,
    • datamining process

Introduction

Information is one of the important criteria and essential factors for an individual to categorize the characteristic of that particular user. Social Media has become one of the great significance of the transmittance of information and news. The main objective for the development of the reign of the internet is to send essential information from one edge of the place to another place. Data transmittance makes the information to enhance in a peculiar way and reach the people. But the Marketing and Firm Companies make use of the data and furnish their advertising product while reaching the destination. Further Machine learning techniques were manipulated and used in the dataset of the users. First, the dataset of the particular user is extracted or multiple data sets of the enormous of the users are extracted. Then the data set are to be extracted for a constant interval of period. The framework initially would be unstructured data. The unstructured data should be interpreted and to be converted to structured data since it has been stored in a big database. It consists of numerous attributes and classes. Classes and attributes are one of the important phenomena that have to be considered under the data extraction process. Once the structured data is received then the data can be disintegrated into a chronological sequence. At the final stage, the extracted structured data is maneuvered with the help of some machine learning techniques and statistical analysis about logging in, logging out, posting photos, or being tagged various criteria the personality assessment of a particular user can be obtained.[1] “Research challenges on Opinion Mining and Sentiment Analysis “furnishes the conceptual perception towards the ascertained toward privacy. From this paper, we have just extracted few theory and the psychology of the users towards social media trafficking[2]We have taken the algorithm not as same but we have made a real-time model it only provides the theoretical analysis[3-5]We have taken the theory-based concepts and also provides the backend process of how the privacy of users are used as data.[8] paper mainly concepts about facebook manipulation and how it tends the users to access it. Its user experience is clearly explained.[18-20]. is the main conceptual paper we have extracted for our reference purpose.

Conclusion

There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. When rule-based spam filtering is done, it fails to track the latest tricks adopted by spammers. Multi-Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are powered by ML. Over 325, 000 malware are detected every day and each piece of code is 90–98% similar to its previous versions. The system security programs that are powered by machine learning understand the coding pattern. Therefore, they detect new malware with 2–10% variation easily and offer protection against them. A number of websites nowadays offer the option to chat with customer support representatives while they are navigating within the site. However, not every website has a live executive to answer your queries. In most of the cases, you talk to a chatbot. These bots tend to extract information from the website and present it to the customers. Meanwhile, the chatbots advance with time. They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms. Therefore by considering the best classification method we can enhance the respective realtime application with the best accuracy rate.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:

Analyzing Product Usage Based on Twitter Users Based on Datamining Process

Bibliography

author

S. Umamaheswari and K. Harikumar,

Year

2020

Title

Analyzing Product Usage Based on Twitter Users Based on Datamining Process

Publish in

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2020, pp. 426-430,

Doi

10.1109/ICCAKM46823.2020.9051488.

PDF reference and original file: Click here

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.