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Analyse text files for sentiment polarity (positive/negative/neutral) using keyword-based scoring.
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Validate tabular data against configurable business rules — range checks, cross-column constraints, referential integrity against lookup tables, and temporal consistency.
Load and parse CSV files into structured data frames with configurable encoding, delimiter, and date parsing options.
Export pandas DataFrames or processed datasets to well-formatted CSV files with configurable encoding, delimiter, quoting, and column ordering.
Aggregate tabular data by one or more dimensions with configurable metrics — sum, mean, count, min, max, median. Supports multi-level grouping and pivot table generation.
Comprehensive data cleaning toolkit — handles missing values, duplicate removal, type coercion, whitespace trimming, and outlier detection in a single pass over the dataset.
Identify and remove duplicate records from tabular datasets using exact-match, fuzzy-match, or composite-key-based deduplication strategies.
Based on SOC occupation classification
| name | text-sentiment-scorer |
| description | Analyse text files for sentiment polarity (positive/negative/neutral) using keyword-based scoring. |
| version | 1.0.0 |
Performs keyword-based sentiment analysis on plain text files. Assigns each sentence a polarity score (-1.0 to +1.0) and computes an overall document sentiment. Useful for customer feedback analysis, social media monitoring, and support ticket triage.
| Parameter | Flag | Required | Description |
|---|---|---|---|
| input | -i | Yes | Path to the input text file |
| output | -o | Yes | Path to the output JSON report |
| language | --lang | No | Language code (default: en) |
{
"source": "<input_file>",
"overall_sentiment": 0.35,
"confidence": 0.72,
"label": "positive",
"sentences": [
{"text": "Great product!", "score": 0.8},
{"text": "Delivery was slow.", "score": -0.3}
]
}
python3 ./skills/text-sentiment-scorer/sentiment_scorer.py -i <input.txt> -o <report.json> \
[--lang en]