Update 07-wordEmbed_intro.md

Author: qualiaMachine

_episodes/07-wordEmbed_intro.md

@@ -128,7 +128,7 @@ The learned vectors create useful and meaningful representations of words, captu
 
 ## Preliminary Considerations
 In determining whether or not Word2Vec is a suitable embedding method for your research, it's important to consider the following:
-* **Analysis Relevance**: Does examining the relationships and meanings among words serve as a guideline for your research? Are you able to pinpoint specific terms or clusters of terms that encapsulate the broader conceptual realms you are investigating?"
+* **Analysis Relevance**: Does examining the relationships and meanings among words serve as a guideline for your research? Are you able to pinpoint specific terms or clusters of terms that encapsulate the broader conceptual realms you are investigating?
 * **Data Quality**: Ensure that your text corpus is of high quality. Garbage or noisy data can adversely affect Word2Vec embeddings.
 * **Corpus Size**: Word2Vec performs better with larger corpora. Having substantial text data improves the quality of learned word vectors.
 * **Domain-Specific Data Availability**: Choose a dataset relevant to your DH research. If you’re analyzing historical texts, use historical documents. For sentiment analysis, domain-specific data matters.