Basic Sentiment Analysis |
Description | Basic sentimental analysis is the mining of textual data to extract subjective information from the source. This helps businesses to understand the social sentiment about their product, service, or brand based upon the monitoring of online conversations. |
Why to use | Textual Analysis – Determine Sentiment score |
When to use | When a sentiment score needs to be determined to make certain business decisions. | When not to use | On numerical data. |
Prerequisites | Clean data using textual pre-processing algorithms. |
Input | - | Output | - |
Statistical Methods used | - | Limitations | It cannot be used on Numerical data. |
Basic sentimental analysis is the mining of textual data to extract subjective information from the source. This helps businesses to understand the social sentiment about their product, service, or brand based upon the monitoring of online conversations.
This algorithm treats texts as Bags of Words (BOW), where the order of words and as such context is ignored. The original text is filtered down to only the words that are thought to carry sentiment. Further, these sentences are classified as positive, negative, or neutral. The sentiment score is determined based on the positive and negative lexicons present in the textual data. You can find the predefined set of positive and negative lexicons in NLTK library.
A sentiment score is calculated to determine the intensity of the sentiment. For example, positive sentiment ranges between 0.01 to 1, with 0.01 being the weakest sentimental score, and 1 being the strongest. For negative sentiment, the range is from -0.01 to -1. A sentiment score of -0.01 is weakly negative, and -1 is strongly negative. The value 0 represents a neutral statement.
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