onomot, a sentiment analysis mobile application, is mainly developed to determine the sentiment of Bangla texts. With the help of this application a user can analyze a text to determine its polarity, subjectivity and the emotion behind it.
The polarity of the text is categorized into the following groups: strongly negative, negative, neutral, strongly positive and positive. The type of the text is essentially determined by the polarity.
The subjectivity of the text is defined using the two classes: subjective and objective.
Actually, subjectivity describes the type of information in the text, including facts and personal opinions.
In addition, the user can also view the emotion of the given text and six categories will be used to classify the text's emotions such as, happy, fear, anger, sadness, surprise and neutral. With this emotion analysis, the user can identify what emotion the text is actually expressing.
To determine the underlying sentiment of a text, the user needs to give a text input, which can either be a sentence or a paragraph but the text must be in bangla and within a given limit.
Through analysis, a polarity score and the polarity of the text will be visible to the user to see whether the particular text is positive, negative or neutral and also the emotion behind the text.
The text having objective nature will not contain any polarity as it is factual information but the subjective text will demonstrate its polarity in the aforementioned classes. Unlike polarity the text will exhibit its emotion for both the subjective or objective text.
After classification, a visualization box that displays sentiment and emotion will emerge at the bottom of the app, and the user will observe that some texts have been highlighted in various colors, as those texts indicates that it has a significant influence on the model's score .The color intensity depends actually on the score, the higher the score level the more intense the color will be.
The polarity of the text is categorized into the following groups: strongly negative, negative, neutral, strongly positive and positive. The type of the text is essentially determined by the polarity.
The subjectivity of the text is defined using the two classes: subjective and objective.
Actually, subjectivity describes the type of information in the text, including facts and personal opinions.
In addition, the user can also view the emotion of the given text and six categories will be used to classify the text's emotions such as, happy, fear, anger, sadness, surprise and neutral. With this emotion analysis, the user can identify what emotion the text is actually expressing.
To determine the underlying sentiment of a text, the user needs to give a text input, which can either be a sentence or a paragraph but the text must be in bangla and within a given limit.
Through analysis, a polarity score and the polarity of the text will be visible to the user to see whether the particular text is positive, negative or neutral and also the emotion behind the text.
The text having objective nature will not contain any polarity as it is factual information but the subjective text will demonstrate its polarity in the aforementioned classes. Unlike polarity the text will exhibit its emotion for both the subjective or objective text.
After classification, a visualization box that displays sentiment and emotion will emerge at the bottom of the app, and the user will observe that some texts have been highlighted in various colors, as those texts indicates that it has a significant influence on the model's score .The color intensity depends actually on the score, the higher the score level the more intense the color will be.
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