The concept of sequential pattern mining for text

Maylawati, Dian Sa’adillah; Aulawi, Hilmi; Ramdhani, Muhammad Ali

doi:10.1088/1757-899x/434/1/012042

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Thus, Natural Language Processing (NLP) has become an importance technique used in Arti cial intelligence for text and/or speech data analyses and analytics [18]. One improved approach in more recent time for text mining processes is sequential pattern mining, which is structured to prepare text representations [19]. One predominant approach is also through word similarity measurements in a sentence.…”

Section: Related Work On Social Opinion Mining For Churn Predictionmentioning

confidence: 99%

Sequentially Clustered Social Opinion for Improved Customer Management in Churn prediction

Ibitoye

Onifade

2022

Preprint

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As business competition continues to increase in different sectors of the economy, chum prediction models as a social behaviour have continued to impact the classification of customers for enhanced decision support and recommendations through text analytics. While it is generally believed that user’s opinion can be gradually influenced by friends on the social media, the prospect of a churning customer increases proportionately when the friend churns. However, oftentimes, during media exchange on the social network, customers are treated distinctly for positive, negative and neutral opinion classifications. By this, the network structure, sequential conversion and its influence on the user community content is neglected for churn prediction. Thus, by developing an opinion-based churn prediction model through sequential content clustering, this research explores user’s media conversation to understand the intrinsic mechanisms of sequentially formulated opinion in a social network community. Consequently, the computational similarities and sentiment between text were extracted to build an opinion driven churn prediction community for enhanced targeted customer acquisition and retention support. The essence is to harness the power of the social networking as replica for mouth-to-mouth conversation on products brands in Natural Language Processing. The research obtained 95%, 87%, and 94% in Recall, Precision, and Accuracy respectively when compared with opinion mining from ordinary data clustering.

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Section: Related Work On Social Opinion Mining For Churn Predictionmentioning

confidence: 99%

Sequentially Clustered Social Opinion for Improved Customer Management in Churn prediction

Ibitoye

Onifade

2022

Preprint

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“…In this case, the temporal aspect is not assigned with much weight. In the case of mining meaningful patterns from the database of customer purchase records, each transaction is considered as an itemset and the transactions are sorted based on the time of the transactions before a pattern mining algorithm is applied [18].…”

Section: Research On Pattern Miningmentioning

confidence: 99%

A Pattern-Mining Driven Study on Differences of Newspapers in expressing Temporal Information

Fu¹,

Dhonnchadha²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper studies the differences between different types of newspapers in expressing temporal information, which is a topic that has not received much attention. Techniques from the fields of temporal processing and pattern mining are employed to investigate this topic. First, a corpus annotated with temporal information is created by the author. Then, sequences of temporal information tags mixed with part-of-speech tags are extracted from the corpus. The TKS algorithm is used to mine skip-gram patterns from the sequences. With these patterns, the signatures of the four newspapers are obtained. In order to make the signatures uniquely characterize the newspapers, we revise the signatures by removing reference patterns. Through examining the number of patterns in the signatures and revised signatures, the proportion of patterns containing temporal information tags and the specific patterns containing temporal information tags, it is found that newspapers differ in ways of expressing temporal information.

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“…Basically, SPM algorithm produces sequential pattern that has several pattern, among others [13], [15], [20], [82]: itemset which is a set of items that are not empty, for example itemset denoted by i, where i = (i j , i j+1 , i j+2 , ..., i n ) and i j are item, then the sequence is an ordered list of several itemsets, if the sequence is denoted by s, then s = {s j , s j+1 , s j+2 , ..., s n } where s j is an itemset. A sequence A = {a 1 , a 2 , a 3 , ..., a n } is said to be subsequence of sequence B = {b 1 , b 2 , b 3 , ..., b n } and B is supersequence of A, if integers i 1 < i 2 < i 3 < ... < i n and a 1 ⊆ b i1 , a 2 ⊆ b i2 , a 3 ⊆ b i3 , ..., a n ⊆ b in .…”

Section: Structured Representation For Textmentioning

confidence: 99%

“…Sequential Pattern Mining (SPM) is a mining technique to find sequence of item in database transaction [13]- [16]. But, in Text Mining, Sequential Pattern Mining is used to prepare text representation that is commonly called Sequence of Words (SoW) [17]- [20]. Deep Learning is a popular method that is developed from Artificial Neural Networks (ANN) with multilayer between input layer and output layer [21]- [23] as illustrated in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

Maylawati¹

2019

IJATCSE

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Readability is a big challenge that must be solved in automatic text summarization research. The aim of this study is to comprehensively and systematically investigate the literatures that are related with text summarization needs, in particular, to prepare efficient algorithm for Indonesian text using Deep Learning and Sequential Pattern Mining. Evidence from previous literatures shows that there are several studies which use Sequential Pattern Mining to extract representation of text for document clustering and classification of Indonesian text. However, not much attention was given to text representation in document summarization, specifically for Indonesian language. As readability is a major concern in the text summarization community, determining a better text representation to maintain the meaning of the generated text summary is deemed necessary. This paper gives an opportunity to take a deeper look into how to design an efficient and effective text representation which can further enhance text summarization readability. Besides the general systematic literature review, we discuss an idea to combine Deep Learning and Sequential Pattern Mining to improve readability of summary result to develop in the future.

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The concept of sequential pattern mining for text

Cited by 7 publications

References 18 publications

Sequentially Clustered Social Opinion for Improved Customer Management in Churn prediction

Sequentially Clustered Social Opinion for Improved Customer Management in Churn prediction

A Pattern-Mining Driven Study on Differences of Newspapers in expressing Temporal Information

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

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