diff --git a/README.md b/README.md
index 7b88c2e..8c21878 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-<h2 align="middle">weighted-random-item-sampler</h2>
+<h2 align="middle">Weighted Random Item Sampler</h2>
 
 The `WeightedRandomItemSampler` class implements a random sampler where the probability of selecting an item is proportional to its weight.
 
@@ -13,17 +13,17 @@ Use case examples include:
 * __Anomaly Detection__: Sample data points from a dataset with weights based on their anomaly scores for further analysis.
 * __ML Model Training__: Select training samples with weights based on their importance or difficulty to ensure diverse and balanced training data.
 
-## Key Features
+## Key Features :sparkles:
 
 - __Weighted Random Sampling__: Sampling items with proportional probability to their weight.
-- __Efficiency__: O(log(n)) time and O(1) space per sample, making this class suitable for performance-demanding applications where the set of items is large and the sampling frequency is high.
-- __Comprehensive documentation__: The class is thoroughly documented, enabling IDEs to provide helpful tooltips that enhance the coding experience.
+- __Efficiency :gear:__: O(log(n)) time and O(1) space per sample, making this class suitable for performance-demanding applications where the set of items is large and the sampling frequency is high.
+- __Comprehensive documentation :books:__: The class is thoroughly documented, enabling IDEs to provide helpful tooltips that enhance the coding experience.
 - __Tests__: Fully covered by unit tests.
 - No external runtime dependencies: Only development dependencies are used.
 - ES2020 Compatibility: The `tsconfig` target is set to ES2020, ensuring compatibility with ES2020 environments.
 - TypeScript support.
 
-## Use Case Example
+## Use Case Example :man_technologist:
 
 Consider a component responsible for selecting training-samples for a ML model. By assigning weights based on the importance or difficulty of each sample, we ensure a diverse and balanced training dataset.
 
@@ -60,6 +60,6 @@ class ModelTrainer {
 }
 ```
 
-## License
+## License :scroll:
 
 [Apache 2.0](LICENSE)
diff --git a/package-lock.json b/package-lock.json
index 09cfe77..0c0b936 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "weighted-random-item-sampler",
-  "version": "1.0.0",
+  "version": "1.0.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "weighted-random-item-sampler",
-      "version": "1.0.0",
+      "version": "1.0.1",
       "license": "Apache-2.0",
       "devDependencies": {
         "@types/jest": "^29.5.12",
diff --git a/package.json b/package.json
index cb2420d..438dc35 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "weighted-random-item-sampler",
-  "version": "1.0.0",
+  "version": "1.0.1",
   "description": "A weighted random item sampler (selector), where the probability of selecting an item is proportional to its weight. The sampling method utilizes a binary search optimization, making it suitable for performance-demanding applications where the set of items is large and the sampling frequency is high.",
   "repository": {
     "type": "git",
@@ -20,15 +20,15 @@
   "keywords": [
     "weight",
     "weighted",
+    "weighted-random",
     "sample",
     "sampler",
     "sampling",
     "random",
     "randomized",
     "random-select",
-    "select",
-    "selector",
-    "selection",
+    "weighted-select",
+    "weighted-selector",
     "weighted-selection",
     "weighted-sampling",
     "weighted-item",