Semantic Approach for Big Five Personality Prediction on Twitter

2022

Social media such as Twitter has now become very close to society. Twitter users can express current issues, their opinions, product reviews, and many other things both positive and negative. Twitter is also used by companies to monitor the assessment of their products among the public as insight that will be used to evaluate what aspects of their products need to be further developed. Twitter with its limitation of only allowing users to post a maximum tweet of 280 characters will make a lot of abbreviated and difficult to understand words used, so it will allow vocabulary mismatch problems to occur. Therefore, in this paper, research conducted on aspect-based sentiment analysis of Telkomsel’s products from the aspects of signal and service by applying feature expansion using Fasttext word embedding to overcome vocabulary mismatch problem and classification with the Support Vector Machine (SVM) method. Sampling technique with Synthetic Minority Oversampling Technique (SMOTE) used to overcome data imbalance. The experimental results show that feature expansion can increase the performance of model. The final results obtained F1-Score value of the model for the signal aspect increased by 27.91% with F1-Score 95.93%, and for the service aspect increased by 42.36% with F1-Score 94.53%.

Section: Support Vector Machinementioning

confidence: 99%

Aspect Based Sentiment Analysis with FastText Feature Expansion and Support Vector Machine Method on Twitter

Raihan

2022

“…Another research was conducted by Salsabila et al [7]. used a dataset of 295 users and 511,617 tweets using the Based on previous research, the results used the Naïve Bayes Support Vector Machine produced better accuracy values than the Support Vector Machine method.…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes-Support Vector Machine Combined BERT to Classified Big Five Personality on Twitter

Martani

2022

Twitter is one of the most popular social media used to interact online. Through Twitter, a person's personality can be determined based on that person's thoughts, feelings, and behavior patterns. A person has five main personalities likes Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. This study will make five personality predictions using the Naïve Bayes method – Support Vector Machine, Synthetic Minority Over Sampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder from Transformers Representations (BERT). A questionnaire was distributed to people who used Twitter to collect and become a dataset in this research. The dataset obtained will be processed into SMOTE to balance the data. Linguistic Inquiry Word Count is used as a linguistic feature and BERT will be used as a semantic approach. The Naïve Bayes method is used to perform the weighting and the Support Vector Machine is used to classify Big Five Personalities. To help improve accuracy, the Optuna Hyperparameter Tuning method will be added to the Naïve Bayes Support Vector Machine model. This study has an accuracy of 87.82% from the results of combining SMOTE, BERT, LIWC, and Tuning where the accuracy increases from the baseline.

“…The Big Five personality traits are considered an effective way to determine a person's personality because they are more informative. [4]. The Big Five personality traits are often abbreviated as the "OCEAN" model, openness, conscientiousness, extroversion, agreeableness, and neuroticism [1].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies try to measure a person's personality through the Twitter user word classification method. One of them is research conducted [4], The author tries to use the LIWC method to count words automatically based on the category, then use the Support Vector Machine (SVM) method to classify the Big Five Personalities. This study produces a model that can predict a person's personality by 80.07%.…”

Section: Introductionmentioning

confidence: 99%

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Big Five Personality Assessment Using KNN method with RoBERTA

Aryani

2022

Personality is the general way a person responds to and interacts with others. Personality is also often defined as the quality that distinguishes individuals. Social media was created to help people communicate remotely and easily. These personalities fall into five categories known as the Big Five personality traits, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN). The use of K-Nearest Neighbour (KNN) is a method of classifying objects based on the training data closest to them. To overcome the data imbalance during training data, we use K-Means SMOTE (Synthetic Minority Oversampling Technique). Other features such as LIWC (Linguistic Inquiry Word Count), Information Gain, Robustly Optimized BERT Approach (RoBERTa), and hyperparameter tuning can improve the performance of the systems we build. The focus of this study is to present an analysis of Twitter user behavior that can be used to predict the personality of the Big Five Personality using the KNN method. The Important aspect to consider when using this method, namely accuracy in classifying the Big Five Personalities. The experimental results show that the accuracy of the KNN method is 72.09%, which is 95.28% gain above the specified baseline.