Sentiment-Analysis-Project-

SentimentAnalysisProjectDocumentation

Overview:

TheSentimentAnalysisprojectaimstoanalyzethesentimentofuser-providedtextorURLs. Theapplicationprovidesuserswithinsightsintothesentimentscore,sentimentlabel(positive ornegative),andthetopreasonsforscoreincrementanddecrement.

Features:

SentimentAnalysis:

● UserscanentertextorURLsforsentimentanalysis.
● TheapplicationusesNaturalLanguageProcessing(NLP)techniquestocalculatethe
sentimentscore.
● Sentimentlabels(positiveornegative)areprovidedbasedonthesentimentscore.

TopReasons:

● Theapplicationliststhetop 5 reasonsforbothscoreincrementanddecrement.
● Usersreceiveinsightsintowhatfactorscontributedtochangesinsentiment.

WebScraping:

● TheprojectincludeswebscrapingfunctionalitytofetchdatafromaspecifiedURL.
● Thescrapeddatacontributestotheoverallsentimentanalysisscore.

User-FriendlyInterface:

● Thewebinterfaceisdesignedtobeuser-friendlywithacleanandanimatedlayout.
● Userscaneasilyunderstandthesentimentanalysisresultsandreasons.

ProjectStructure

TheprojectfollowsaFlaskwebapplicationstructurewiththefollowingkeycomponents:

● app/:
○ __init__.py:InitializestheFlaskapplication.
○ routes.py:Definestheapplicationroutesandhandlesuserrequests.

○^1 utils.py:Containsutilityfunctionsforsentimentanalysisandwebscraping.

● run.py:ExecutestheFlaskapplication.
● templates/:
○ index.html:TheHTMLfilefortheuserinterface,incorporatingthedesignand
interactivityitalsocontainsadditionalstylingfortheapplicationwithcss.

GettingStarted

Prerequisites ● Python3.x ● Installprojectdependenciesbyrunning:pipinstall-rrequirements.txt ● RunningtheApplication

RunningtheApplication

1. Navigatetotheprojectdirectory.
2. RuntheFlaskapplication: Bash: pythonrun.py

Theapplicationwillbeaccessibleathttp://127.0.0.1:5000/inyourwebbrowser.

Usage

1. EntertextoraURLintotheprovidedinputareaonthehomepage.
2. Clickthe"AnalyzeSentiment"button.
3. Viewthesentimentanalysisresults,includingthesentimentscore,label,andtop reasonsforscorechanges.

Drawbacks

Despitetheproject'scapabilities,therearesomedrawbackstobeawareof:

1. Real-TimeImplementationChallenges :

a. Real-timesentimentanalysismayfacechallengesduetothedynamicnatureof
onlinecontent.
b. Theprojectmaynotcaptureinstantsentimentchangesoremergingtrendsin
real-time.

2. ScoreGenerationComplexity :

a. Generatingsentimentscoresbasedonwebscrapinganduserinputinvolves
inherentcomplexities.
b. Factorssuchasdiversecontenttypesandevolvinglanguagepatternscan
impacttheaccuracyofsentimentscores.

Screenshots

BelowarescreenshotsshowcasingtheSentimentAnalysiswebapplication:

Figure1:NegativeScore

Figure2:PositiveScore

Figure3:NeutralScore(onaLinkedinprofile)

Conclusion

TheSentimentAnalysisprojectprovidesuserswithavaluabletooltounderstandthesentiment oftextorcontentonspecifiedURLs.Whiletheapplicationhasnotablefeatures,usersshould bemindfulofthelimitations,particularlyinreal-timeimplementationandscoregeneration complexities.