Automatic Personality Prediction Sample Clauses

Automatic Personality Prediction. ‌ Automatic personality prediction is a task of identifying a person’s personalities based on the language input. The Big Five Hypothesis is typically used for the task[3, 4]. This hypothesis describes one’s personalities from five dimensions: Extraversion, Agreeableness, Conscientious- ness, Neuroticism, and Openness to Experience. We will describe the hypothesis in more details in the Chapter 2. There are two variations of the task in our project. One is to identify the personalities of the writer by reading a monologue essay. The other version is to identify the personalities of a speaker given the conversation corpus in which the speaker is involved in. The second version is inherently more challenging [17] for human beings than the first one because it needs a participant to grasp the interpersonal relationships and other contextual information from a short conversation before he or she could make a reasonable judgment. Some studies [8, 17] have provided multimodal information such as audio data along with the conversation text for the participants and show that prosodic information is helpful for personality identification. In our study, we focus only on text data and explores the boundaries of using only text for the task. Therefore, our task can be seen as a variation of text classification [1, 9]: the main difference is that we are identifying the personalities of a writer or speaker behind the text, whereas typical text classification problems such as sentiment analysis focus on classifying the content of the text. This situation naturally makes our task more challenging than traditional text classification problems. The contextual information is necessary for automatic personality prediction. Therefore, we need to extract not only local features such as n-grams but also global features from the corpus. For the Essays data, we want to pay attention to the important linguistic cues by ignoring noise. For the Friends data, we want the model to pay attention to the utterances of the target speaker. To achieve this goal in both datasets, attention mechanism is added to both CNN and LSTM models.
Sample 1
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