JohnSnowLabs
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+---
+layout: model
+title: Sentiment Analysis pipeline for English
+author: John Snow Labs
+name: analyze_sentiment
+date: 2023-05-25
+tags: [open_source, english, analyze_sentiment, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1685040876208.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1685040876208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')
+
+result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")
+
+val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
+result_df = nlu.load('en.classify').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')
+
+result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")
+
+val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
+result_df = nlu.load('en.classify').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | text                                                                                                                                                                                                                                                                      | sentiment   |
+|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------|
+|  0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive    |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|analyze_sentiment|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|5.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel
+- ViveknSentimentModel
@@ -0,0 +1,122 @@
+---
+layout: model
+title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb)
+author: John Snow Labs
+name: analyze_sentimentdl_glove_imdb
+date: 2023-05-25
+tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1685051273135.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1685051273135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.sentiment.glove').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('en.sentiment.glove').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | tokens                                         | word_embeddings              | sentence_embeddings          | sentiment   |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------|
+|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg']     |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|analyze_sentimentdl_glove_imdb|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|161.6 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- SentenceEmbeddings
+- SentimentDLModel
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+---
+layout: model
+title: Spell Checking Pipeline for English
+author: John Snow Labs
+name: check_spelling
+date: 2023-05-25
+tags: [open_source, english, check_spelling, pipeline, en]
+task: Spell Check
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.4
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+        and recognizes entities .
+         It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1685039983204.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1685039983204.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('check_spelling', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("check_spelling", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('').predict(text)
+result_df
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('check_spelling', lang = 'en')
+annotations =  pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("check_spelling", lang = "en")
+val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hello from John Snow Labs ! ""]
+result_df = nlu.load('').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                         | sentence                        | token                                          | checked                                        |
+|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------|
+|  0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|check_spelling|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|906.2 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel