Analisis Sentimen Kurikulum 2013 Pada Twitter Menggunakan Ensemble Feature Dan Metode K-Nearest Neighbor

Kurikulum 2013 merupakan kurikulum baru dalam sistem pendidikan Indonesia yang telah diberlakukan oleh pemerintah untuk menggantikan kurikulum 2006 atau Kurikulum Tingkat Satuan Pendidikan (KTSP). Diberlakukannya kurikulum ini pada beberapa tahun terakhir memicu berbagai kontroversi dalam dunia pendidikan Indonesia terutama di kalangan pelajar, hal-hal seperti siswa yang dituntut lebih aktif, jam pelajaran yang ditambah dan hal-hal lainnya yang menyebabkan muncul berbagai opini yang berkembang di masyarakat terutama pada media sosial Twitter. Diperkirakan sekitar 200 juta pengguna Twitter melakukan posting 400 juta tweet per hari. Dalam penelitian ini, dilakukan analisis sentimen untuk mengetahui opini yang berkembang tersebut yang dibagi ke dalam opini positif atau opini negatif. Fitur dan metode yang digunakan adalah ensemble feature dan metode klasifikasi K-Nearest Neighbor (K-NN). Ensemble feature merupakan fitur gabungan, berupa fitur statistik Bag of Words (BoW) dan semantik (twitter specific, textual features, PoS features, lexicon based features). Berdasarkan serangkaian pengujian, kombinasi fitur berdampak dalam meningkatkan akurasi metode K-Nearest Neighbor (K-NN) untuk menentukan opini positif atau negatif. Penggabungan fitur ini dapat melengkapi kelemahan masing-masing fitur, sehingga hasil akhir akurasi yang didapatkan dengan menggabungkan kedua fitur tersebut mecapai 96%. Berbeda hal jika hanya menggunakan fitur secara independen saja, akurasi yang didapatkan hanya mencapai 80% pada fitur Bag of Words (BoW) dan 82% pada fitur ensemble tanpa Bag of Words (BoW)

Generating a Malay sentiment lexicon based on wordnet

Sentiment lexicon is a list of vocabularies that consists of positive and negative words. In opinion mining, sentiment lexicon is one of the important source in text polarity classification task in sentiment analysis model. Studies in Malay sentiment analysis is increasing since the volume of sentiment data is growing on social media. Therefore, requirement in Malay sentiment lexicon is high. However, Malay sentiment lexicon development is a difficult task due to the scarcity of Malay language resource. Thus, various approaches and techniques are used to generate sentiment lexicon. The objective of this paper is to develop Malay sentiment lexicon generation algorithm based on WordNet. In this study, the method is to map the WordNet Bahasa with English WordNet to get the offset value of a seed set of sentiment words. The seed set is used to generate the synonym and antonym semantic relation in English WordNet. The highest result achives 86.58% agreement with human annotators and 91.31% F1-measure in word polarity classification. The result shows the effectiveness of the proposed algorithm to generate Malay sentiment lexicon based on WordNet

Developing Cross-Lingual Sentiment Analysis Of Malay Twitter Data Using Lexicon-Based Approach

Sentiment analysis is a process of detecting and classifying sentiments into positive, negative or neutral. Most sentiment analysis research focus on English lexicon vocabularies. However, Malay is still under-resourced. Research of sentiment analysis in Malaysia social media is challenging due to mixed language usage of English and Malay. The objective of this study was to develop a cross-lingual sentiment analysis using lexicon based approach. Two lexicons of languages are combined in the system, then, the Twitter data were collected and the results were determined using graph. The results showed that the classifier was able to determine the sentiments. This study is significant for companies and governments to understand people's opinion on social network especially in Malay speaking regions

MELex: a new lexicon for sentiment analysis in mining public opinion of Malaysia affordable housing projects

Sentiment analysis has the potential as an analytical tool to understand the preferences of the public. It has become one of the most active and progressively popular areas in information retrieval and text mining. However, in the Malaysia context, the sentiment analysis is still limited due to the lack of sentiment lexicon. Thus, the focus of this study is to a new lexicon and enhance the classification accuracy of sentiment analysis in mining public opinion for Malaysia affordable housing project. The new lexicon for sentiment analysis is constructed by using a bilingual and domain-specific sentiment lexicon approach. A detailed review of existing approaches has been conducted and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. The developed approach is able to analyze text for two most widely used languages in Malaysia, Malay and English, with better accuracy. The process of constructing MELex involves three activities: seed words selection, polarity assignment and synonym expansions, with four different experiments have been implemented. It is evaluated based on the experimentation and case study approaches where PR1MA and PPAM are selected as case projects. Based on the comparative results over 2,230 testing data, the study reveals that the classification using MELex outperforms the existing approaches with the accuracy achieved for PR1MA and PPAM projects are 90.02% and 89.17%, respectively. This indicates the capabilities of MELex in classifying public sentiment towards PRIMA and PPAM housing projects. The study has shown promising and better results in property domain as compared to the previous research. Hence, the lexicon-based approach implemented in this study can reflect the reliability of the sentiment lexicon in classifying public sentiments

Visualisasi pohon sintaksis berasaskan model dan algoritma sintaks ayat bahasa Melayu

Previous works that produce syntactic tree output has disregarded additional relevant components such as sentence checking, sentence correction, the syntax tree visualization and the words attributes of each sentence. As such, this study aims at producing an algorithm for syntactic tree output enhancement from which the relevant output component mentioned above can be produced. The additional components namely sentence checking, sentence correction, syntax tree visualization (VPS) and word attribute are modelled into a package prior to translating them into a tangible output. In term of rules, previous studies have used phrase-structure rules (RSF) in analysing the Malay sentence. But RSF has been found to be a non-universal formula. Our work has brought us to the introduction of X-bar rules for BM VPS, which consequently becomes one of the contributions of this study. To achieve these objectives (the algorithm, the model and the X-bar rules), five phases of research methods involved namely identifying the research gap, the sentence and rules categorization, model and algorithm design phase, prototype development evaluation and conclusion phase. Parseval assessment method, which is an output evaluation method in natural language processing, was used for the evaluation. Point of analysis were the recall and precision valuation metrics. For VPS output, the average results obtained were 100% for recall and 97.8% for precision. For sentence correction, the results given were 100% for recall and 87.8% for precision. These results proved that the algorithm and model, for syntactic tree output enhancement, are generalisable enough to be tested on other languages. User evaluation on the prototype was also performed yielding in the average subjective satisfaction of 87.9% and a mean score of 6.157, based on semantic differential scales of 1 to 7. Cognitive assessment was also recorded, obtaining average cognitive score of 84.6% with a mean score of 4.230, on the scale 5. Analysis on those results indicated positive scores on the model-based product specifically on usefulness, ease of use, ease of learning, subjective satisfaction, and cognitive measures. It can be concluded that the algorithm and model proposed were useful for the development of the prototype. The prototype is therefore beneficial as an educational assistance to understand Malay sentences when provided with enhanced output on sentence checking, sentence correction, syntax tree visualization (VPS) and words attribut