Clean text with gensim
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Text mining includes data mining algorithms, NLP, machine learning, and statistical operations to derive useful content from unstructured formats such as social media textual data. In general, the procedure of exploring data to collect valuable information is stated as text mining. In this paper, we focused on the topic modeling (TM) task, which was described by Miriam (2012) as a method to find groups of words (topics) in a corpus of text. Essentially, keyword extraction is the most fundamental task in several fields, such as information retrieval, text mining, and NLP applications, namely, topic detection and tracking ( Kamalrudin et al., 2010). The first actual example of the use of NLP techniques was in the 1950s in a translation from Russian to English that contained numerous literal transaction misunderstandings ( Hutchins, 2004). Natural language processing (NLP) is a field that combines the power of computational linguistics, computer science, and artificial intelligence to enable machines to understand, analyze, and generate the meaning of natural human speech.
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It is convenient to employ a natural approach, similar to a human–human interaction, where users can specify their preferences over an extended dialogue. Furthermore, there is a need to extract more useful and hidden information from numerous online sources that are stored as text and written in natural language within the social network landscape (e.g., Twitter, LinkedIn, and Facebook). Consequently, there is a need for more efficient methods and tools that can aid in detecting and analyzing content in online social networks (OSNs), particularly for those using user-generated content (UGC) as a source of data.
#Clean text with gensim full
The internet is full of information and sources of knowledge that may confuse readers and cause them to spend additional time and effort in finding relevant information about specific topics of interest. Indeed, the internet has increased demand for the development of commercial applications and services to provide better shopping experiences and commercial activities for customers around the world. People nowadays tend to rely heavily on the internet in their daily social and commercial activities. The paper sheds light on some common topic modeling methods in a short-text context and provides direction for researchers who seek to apply these methods. As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results. Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F-score, and topic coherence. These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages.