Uploading Urdu Tweets Dataset

<h1>Dataset Name: Urdu Tweets Dataset</h1>

<h2>Description:</h2>

This dataset is a collection of Urdu text samples gathered from social media and online platforms. It is designed for research in sentiment analysis and emotion detection in the Urdu language—a low-resource language with unique linguistic challenges. The dataset comprises the following columns:

<ul>
<li>Id: A unique identifier for each text sample.</li>
<li>Text: The original Urdu text, containing tweets or user-generated content.</li>
<li>Emotions: A column that holds emotion annotations in a list-like format. Each entry contains one or more emotion labels (e.g., SMILING FACE WITH SMILING EYES, HEAVY BLACK HEART) along with associated numerical scores indicating the intensity of the expressed emotion.</li>
<li>Category: A field that may denote the broader context or domain of the text.</li>
<li>Emoticon: Contains additional emoticon-related information, which can be useful for further sentiment or emotion analysis.</li>
</ul>

<h2>Key Features:</h2>

<ul>
<li>Multi-Label Emotion Annotations: The "Emotions" column provides rich annotations that include both the emotion and its intensity, enabling fine-grained analysis.</li>
<li>Focus on Urdu: This dataset is particularly useful for developing and evaluating sentiment analysis and emotion detection models for Urdu, addressing the challenges of low-resource languages.</li>
<li>Diverse Sources: The texts are sourced from social media, reflecting a wide range of informal language and expressions.</li>
<li>Applicability: Ideal for tasks such as sentiment classification (positive, negative, neutral), emotion detection, and other NLP applications focusing on Urdu.</li>
</ul>

<h2>Potential Applications:</h2>

<ul>
<li>Sentiment Analysis: Classifying texts into positive, neutral, and negative sentiments.</li>
<li>Emotion Detection: Leveraging the detailed emotion scores to detect and analyze emotional intensity.</li>
<li>Social Media Analytics: Understanding public sentiment and emotional responses on social platforms.</li>
<li>Low-Resource Language Research: Providing a valuable resource for developing NLP models in Urdu.</li>
<li>This dataset offers a unique opportunity to explore sentiment analysis in a language that poses distinctive challenges, and it can serve as a benchmark for future research in Urdu NLP.</li>
</ul>

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+Urdu[[:space:]]Tweets[[:space:]]Dataset.xlsx filter=lfs diff=lfs merge=lfs -text

Urdu Tweets Dataset.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db7c724b556e4d27dbc600d17678bb2836db25b8ce486a41a25178ca23fbf93d
+size 99086759