Added CVP to docs

Author: x-tabdeveloping

docs/cvp.md

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+# Concept Vector Projection
+
+Concept Vector Projection is an embedding-based method for extracting continuous sentiment (or other) scores from free-text documents.
+
+<figure>
+  <img src="../images/cvp.png", title="", style="width:1050px;padding:0px;border:none;"></img>
+  <figcaption> Figure 1: Schematic Overview of Concept Vector Projection.<br> <i>Figure from Lyngbæk et al. (2025)</i> </figcaption>
+</figure>
+
+The method rests on the idea that one can construct a _concept vector_ by encoding positive and negative _seed phrases_ with a transformer, then taking the difference of these mean vectors.
+We can then project other documents' embeddings onto these concept vectors by taking the dot product with the concept vector, thereby giving continuous scores on how related documents are to a given concept.
+
+## Usage
+
+### Single Concept
+
+When projecting onto a single concept, you should specify the seeds as a tuple of positive and negative phrases.
+
+```python
+from turftopic import ConceptVectorProjection
+
+positive = [
+    "I love this product",
+    "This is absolutely lovely",
+    "My daughter is going to adore this"
+]
+negative = [
+    "This product is not at all as advertised, I'm very displeased",
+    "I hate this",
+    "What a horrible way to deal with people"
+]
+cvp = ConceptVectorProjection(seeds=(positive, negative))
+
+test_documents = ["My cute little doggy", "Few this is digusting"]
+doc_concept_matrix = cvp.transform(test_documents)
+print(doc_concept_matrix)
+```
+
+```python
+[[0.24265897]
+ [0.01709663]]
+```
+
+### Multiple Concepts
+
+When projecting documents to multiple concepts at once, you will need to specify seeds for each concept, as well as its name.
+Internally this is handled with an `OrderedDict`, which you can either specify yourself, or Turftopic can do it for you:
+
+```python
+import pandas as pd
+from collections import OrderedDict
+
+cuteness_seeds = (["Absolutely adorable", "I love how he dances with his little feet"], ["What a big slob of an abomination", "A suspicious old man sat next to me on the bus today"])
+bullish_seeds = (["We are going to the moon", "This stock will prove an incredible investment"], ["I will short the hell out of them", "Uber stocks drop 7% in value after down-time."])
+
+# Either specify it like this:
+seeds = [("cuteness", cuteness_seeds), ("bullish", bullish_seeds)]
+# or as an OrderedDict:
+seeds = OrderedDict([("cuteness", cuteness_seeds), ("bullish", bullish_seeds)])
+cvp = ConceptVectorProjection(seeds=seeds)
+
+test_documents = ["What an awesome investment", "Tiny beautiful kitty-cat"]
+doc_concept_matrix = cvp.transform(test_documents)
+concept_df = pd.DataFrame(doc_concept_matrix, columns=cvp.get_feature_names_out())
+print(concept_df)
+```
+
+```python
+   cuteness   bullish
+0  0.085957  0.288779
+1  0.269454  0.009495
+```
+
+## API Reference
+
+
+::: turftopic.models.cvp.ConceptVectorProjection
+
+