Combination of Latent Dirichlet Allocation (LDA) and Term Frequency-Inverse Cluster Frequency (TFxICF) in Indonesian text clustering with labeling

Suadaa, Lya Hulliyyatus; Purwarianti, Ayu

doi:10.1109/icoict.2016.7571885

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…Term Frequency-Inverse Cluster Frequency (TF-ICF) [23] is one of the weighting terms based on information from documents on a cluster. It makes this method capable of knowing information about the value of a term in a term.…”

Section: Expanding Term List (Tf-icf)mentioning

confidence: 99%

Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Priyantina¹,

Sarno²

2019

IJIES

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Product reviews are usually determined by sentiment of customers; however sentiment analysis based on aspects still need further research. A hotel commonly has five aspects, which are location, meal, service, comfort and cleanliness. This research proposes methods to determine review sentiment according to the hotel aspects. A hotel reviews are preprocessed into a term list. Firstly, Latent Dirichelet Allocation (LDA) determines the hidden topics of a term list; then Semantic Similarity categorizes the term list based on the topic resulted by Latent Dirichelet Allocation (LDA) into the five aspects of a hotel. Then in calculating similarity measurement, the term list is expanded by using the Term Frequency-Inverse Cluster Frequency (TF-ICF) method. Finally, a classification of customer sentiment (satisfied or dissatisfied) is conducted by using the combination of Word Embedding and Long-short Term Memory (LSTM). The results show that the proposed method can classify the reviews into the five hotel aspects. The highest aspect categorization performance is obtained by using LDA + TF-ICF 100% + Semantic Similarity which reaches 85%; the performance sentiment classification for the highest aspect-based sentiment analysis is obtained by using Word Embedding + LSTM which reaches 93%; and the comfort aspect receives more negative sentiments compared to the sentiments of other aspects. Also the results show that a sentiment is influenced by an aspect.

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Section: Expanding Term List (Tf-icf)mentioning

confidence: 99%

Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Priyantina¹,

Sarno²

2019

IJIES

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show abstract

“…Term Frequency-Inverse Cluster Frequency (TF-ICF) [10] is the method of weighting terms based on information from document on cluster. In general, TF-ICF calculates term frequency from the document on each clusters.…”

Section: Keyword Extraction (Tf-icf)mentioning

confidence: 99%

“…The candidate aspects obtained are categorized with the aspect keywords using word similarity. These keywords are generated using TF-ICF [10] algorithm from Wikipedia.…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Sentiment Analysis Using Grammatical Rules, Word Similarity and SentiCircle

Firmanto¹,

Sarno²

2019

IJIES

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Product reviews can be used as suggestion for companies to improve their services in this digitalization era. These reviews can be presented in more detail using aspect based sentiment analysis. In this research, aspect-based sentiment analysis method is proposed using grammatical rules, word similarity, and SentiCircle. This method began with extracting the candidate aspects rules based on phrase detection in constituency parse. The candidate aspects were categorized using word similarity. Word similarity calculated the similarity value between the candidate aspects and the keywords obtained from Wikipedia. To determine sentiment polarity, SentiCircle is used. SentiCircle can capture the contextual sentiment from the data. The result showed that the proposed method was able to categorize aspects correctly, with the highest f1-measure value of 84%, while sentiment analysis produced the highest f1-measure of 87%.

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“…Pemilihan frasa kandidat dapat menggunakan pendekatan statistik, berbasis graf, atau klasterisasi topik. Pemilihan frasa dengan pendekatan statistik antara lain dengan pembobotan Term Frequency -Inverse Cluster Frequency (TF-ICF) [27], menggunakan perhitungan Markov Chain [11], dan pemberian nilai frasa kandidat berdasarkan Pointwise Mutual Information (PMI) [28]. Pemilihan frasa kandidat berbasis graf sebagai representasi teks seperti TextRank [23]…”

Section: Pelabelan Klaster Dan Klasterisasi Topikunclassified

“…Pertama-tama, distribusi topik dari klaster artikel ilmiah didapatkan dengan metode Latent Dirchlet Allocation (LDA) pada kumpulan dokumen di klaster d [27]. Metode LDA menghasilkan model topik dengan merupakan topik ke-k. Model topik memiliki dua jenis probabilitas topik yang direpresentasikan dalam bentuk vektor, yaitu probabilitas kata terhadap topik dan probabilitas topik terhadap klaster dokumen seperti pada (5).…”

Section: E Ekstraksi Frasa Kunci Dengan Topicrankunclassified

Ekstraksi Frasa Kunci pada Penggabungan Klaster berdasarkan Maximum-Common-Subgraph

Nurilham¹,

Purwitasari²,

Fatichah³

2018

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Document clustering based on topic similarities helps users in searching from a collection of scientific articles. Topic labels are necessesary for describing subjects of the document clusters. Clusters with related subjects or contextual similarities can be merged to produce more descriptive labels. Relations between those words in one context can be modelled as a graph. Instead of single word, this paper proposed cluster labeling of phrases from scientific articles with cluster merging based on graph. The proposed method begins with K-Means++ for clustering the scientific articles. Then, the candidates of word phrases from document clusters are extracted using Frequent Phrase Mining which inspired by Apriori algorithm. Each cluster result has a representation graph from those extracted word phrases. An indicator value from each graph shows any similarities of graph structures which is calculated with Maximum Common Subgraph (MCS). Those clusters are merged if there are any structure similarities between them. Topic labels of clusters are keyword phrases extracted from a representation graph of previous merged clusters using TopicRank algorithm. The merging process which becomes the contribution of this paper is considering topic distribution within clusters for phrase extraction. The proposed method evaluation is performed based on topic coherence of the merged clusters label. The results show that proposed method can improve topic coherence on the merged clusters with MCS graph size percentage as the key factor. Further observation shows that merged cluster labels consistent to MCS graph.

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Combination of Latent Dirichlet Allocation (LDA) and Term Frequency-Inverse Cluster Frequency (TFxICF) in Indonesian text clustering with labeling

Cited by 14 publications

References 11 publications

Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Aspect-Based Sentiment Analysis Using Grammatical Rules, Word Similarity and SentiCircle

Ekstraksi Frasa Kunci pada Penggabungan Klaster berdasarkan Maximum-Common-Subgraph

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