HYBRIDIZATION OF THE NAIVE BAYES CLASSIFICATION METHOD IN THE FRESHWATER FISH SEED SELLER CLASSIFICATION MODEL

Freshwater fish seed sellers play several roles in the supply chain process in the freshwater fish farming business. The role of the seller of freshwater fish seeds in this process is to distribute fish seeds which are one of the upstream sources in the supply chain process. Freshwater fish cultivators must select competent freshwater fish seed sellers so the supply chain process can run well. A large number of freshwater fish seed sellers in the market remind freshwater fish cultivators to choose the quality of the freshwater fish seed seller in terms of seed quality, low prices, shipping that can reach many areas, ergonomic packaging, and others. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for classification. This study aimed to compare the seed seller classification method in which the appropriate pattern of seed seller was identified by hybridization of Naïve Bayes Classifiers (NBCs), and then the researchers conducted performance appraisal and evaluation. The results are beneficial for freshwater fish cultivators and researchers which will enable them to formulate their plans according to the predicted results. The proposed method has produced significant results by achieving a training data accuracy of 82.61% and the testing data accuracy of 73.91%

Implementasi Metode Naïve Bayes untuk Analisis Sentimen Warga Jakarta Terhadap

Kegiatan riset ini bertujuan untuk menganalisis animo masyarakat Indonesia khususnya warga Jakarta atas munculnya transportasi massa umum MRT yang di resmikan oleh Pemerintah di bulan Maret 2019. Tahapan penelitian diawali proses crawling tweet dengan menggunakan tweetscrapper dari python. Kemudian dilakukan Preprocessing sehingga didapatkan data tweet yang siap untuk diproses pada pemisahan data yaitu data training dan data testing. Data training dilakukan proses pembobotan dengan TF-IDF, dan proses pembelajaran dengan naive bayes. Proses ini disebut dengan proses training yang bertujuan untuk menghasilkan model klasfikasi. Model klasifikasi digunakan untuk data testing melakukan proses klasifikasi yang menghasilkan label sentimen (positif/negatif). Proses ini dinamakan dengan proses testing. Hasil testing akan dilakukan perhitungan akurasi dari model yang sudah dibuat. Luaran dari penelitian ini berupa analisis sentimen animo warga Jakarta pada media sosial Twitter terhadap kehadiran layanan transportasi publik MRT, dan akurasi yang dihasilkan oleh metode naïve bayes yang diimplementasikan pada analisis sentime

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments

Stock market classification model using sentiment analysis on twitter based on hybrid naive bayes classifiers

Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%

Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach

Despite the enormous increase in the number of Arabic posts on social networks, the sentiment analysis research into extracting opinions from these posts lags behind that for the English language. This is largely attributed to the challenges in processing the morphologically complex Arabic natural language and the scarcity of Arabic NLP tools and resources. This complex task is further exacerbated when analysing dialectal Arabic that do not abide by the formal grammatical structure. Based on the semantic modelling of the target domain’s knowledge and multi-factor lexicon-based sentiment analysis, the intent of this research is to use a hybrid approach, integrating linguistic and machine learning methods for sentiment analysis classification of dialectal Arabic. First, a dataset of dialectal Arabic tweets was collected focusing on the unemployment domain, which is annotated manually. The tweets cover different dialectal Arabic in Saudi Arabia for which a comprehensive Arabic sentiment lexicon was constructed. This approach to sentiment analysis also integrated a novel light stemming mechanism towards improved Saudi dialectal Arabic stemming. Subsequently, a novel multi-factor lexicon-based sentiment analysis algorithm was developed for domain-specific social media posts written in dialectal Arabic. The algorithm considers several factors (emoji, intensifiers, negations, supplications) to improve the accuracy of the classifications. Applying this model to a central problem of sentiment analysis in dialectical Arabic, these operational techniques were deployed in order to assess analytical performance across social media channels which are vulnerable to semantic and colloquial variations. Finally, this study presented a new hybrid approach to sentiment analysis where domain knowledge is utilised in two methods to combine computational linguistics and machine learning; the first method integrates the problem domain semantic knowledgebase in the machine learning training features set, while the second uses the outcome of the lexicon-based sentiment classification in the training of the machine learning methods. By integrating these techniques into a single, hybridised solution, a greater degree of accuracy and consistency was achieved than applying each approach independently, confirming a pragmatic solution to sentiment classification in dialectical Arabic text