Free Social Media Sentiment Analyzer
Group English comments by sentiment, review the original text and export your checked labels.
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How to use this tool
Get an organized first view of the tone in your English comment set. Paste comments or import a text/CSV file, then compare positive, neutral and negative categories. The Social Media Sentiment Analyzer shows the original wording alongside each suggested label, letting you filter the results, review context and adjust the classification before exporting.
- 01
Add an English comment set
Paste one comment per line or import a TXT/CSV file. For a structured file, use a recognized text column such as comment or text and keep one comment per row.
- 02
Analyze and inspect the categories
Run the analysis and review the totals for positive, neutral and negative. Filter a category to inspect the comments that contribute to the displayed distribution.
- 03
Review labels and export
Read each original comment in context and adjust a suggested label when needed. Download CSV after the review to keep the wording, final classification and edit indicators together.
Begin with a defined group of English comments
Choose comments from the content, campaign or period you want to review. A defined set makes the category totals easier to interpret. Keep a note of where the entries came from and use the English text itself as the material for this analyzer's classification workflow.
Use the distribution as a route into the comments
The summary shows how the supplied comments are distributed among positive, neutral and negative labels. Use that overview to decide which group to read first, then open the original wording in the filtered list. The category counts become more useful when paired with the subjects people actually discuss.
Bring context into the final label
Short comments, humor and mixed opinions benefit from human review. Read the wording with the original conversation in mind and adjust the label to match your interpretation. The confidence display is a review cue, and manually changed entries are marked so the exported record preserves that distinction.
Keep reviewed comments ready for a report
Export the CSV once the labels are checked. Use the original text and final classification to collect examples, prepare a content review or discuss recurring concerns and positive feedback. Add the source, period and review method when presenting the distribution to someone else.
Frequently asked questions
What is a social media sentiment analyzer?
A Social Media Sentiment Analyzer suggests the tone of written comments. SocQ groups English comments into positive, neutral and negative categories and provides original-text review, editable labels and CSV export.
What comments should I prepare for analysis?
Use English comments from a defined content set or period. Paste one per line or prepare a CSV with a recognized comment-text column and one entry per row.
How many comments can I analyze together?
Prepare up to 500 comments, with each comment up to 3,000 characters. Split a larger research set into labeled groups so each result keeps a clear context.
Which labels does the analyzer use?
The categories are positive, neutral and negative. Review the original wording alongside the suggested category before using the label in a report.
Can I adjust a suggested classification?
Yes. Choose another label for an individual comment. The category totals update and the entry is marked as manually reviewed in the exported record.
How should I interpret the confidence score?
Use the score as one cue for reviewing a suggested label. Combine that cue with the actual wording and conversation context when making your final classification.
How do I review one sentiment group?
Select the category in the filter to show the matching entries. Read those comments together and look for the concrete subjects behind the shared tone.
What does the CSV export include?
The export includes comment text, the final label, confidence information for unchanged suggestions and a manual-edit indicator. Keep source and review notes with the file for reporting context.
For developers
Collect public comment data for a defined research set
SocQ comment APIs support applications that gather public discussion data. Prepare a well-defined English comment set from your appropriate source records, then use the analyzer for a reviewed classification workflow.
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